Systems and methods for surgical planning of arthroplasty procedures

ABSTRACT

A method for planning an arthroplasty procedure on a patient bone. The method may include accessing generic bone data stored in a memory of a computer, using the computer to generate modified bone data by modifying the generic bone data according to medical imaging data of the patient bone, using the computer to derive a location of non-bone tissue data relative to the modified bone data, and superimposing implant data and the modified bone data in defining a resection of an arthroplasty target region of the patient bone.

CROSS REFERENCE TO RELATED APPLICATION

The present application is a continuation-in-part application of U.S.patent application Ser. No. 16/229,997, filed Dec. 21, 2018, which is acontinuation application of U.S. application Ser. No. 15/581,974 filedApr. 28, 2017, now U.S. Pat. No. 10,159,513, which application is acontinuation of U.S. application Ser. No. 14/946,106 filed Nov. 19,2015, now U.S. Pat. No. 9,687,259, which application is a continuationof U.S. application Ser. No. 13/731,697 filed Dec. 31, 2012, now U.S.Pat. No. 9,208,263, which application is a continuation of U.S.application Ser. No. 13/374,960 filed Jan. 25, 2012, now U.S. Pat. No.8,532,361, which application is a continuation of U.S. patentapplication Ser. No. 13/066,568, filed Apr. 18, 2011, now U.S. Pat. No.8,160,345, which application is a continuation-in-part application ofU.S. patent application Ser. No. 12/386,105 filed Apr. 14, 2009, nowU.S. Pat. No. 8,311,306. U.S. application Ser. No. 12/386,105 claims thebenefit under 35 U.S.C. § 119(e) of U.S. Provisional Patent ApplicationNo. 61/126,102, entitled “System and Method For Image Segmentation inGenerating Computer Models of a Joint to Undergo Arthroplasty” filed onApr. 30, 2008.

The present application is also a continuation-in-part of U.S. patentapplication Ser. No. 16/017,320, filed Jun. 25, 2018, which is acontinuation application of U.S. patent application Ser. No. 15/802,137,filed Nov. 2, 2017, now U.S. Pat. No. 10,034,714, which is acontinuation application of U.S. patent application Ser. No. 15/469,171,filed Mar. 24, 2017, now U.S. Pat. No. 9,839,485, which is acontinuation application of U.S. patent application Ser. No. 15/242,312,filed Aug. 19, 2016, now U.S. Pat. No. 9,636,120, which is a divisionalapplication of U.S. patent application Ser. No. 14/476,500, filed Sep.3, 2014, now U.S. Pat. No. 9,451,970, which is a continuationapplication of U.S. patent application Ser. No. 13/731,850, filed Dec.31, 2012, now U.S. Pat. No. 8,961,527, which is a continuationapplication of U.S. patent application Ser. No. 12/505,056, filed Jul.17, 2009, now U.S. Pat. No. 8,777,875, which claims priority under 35U.S.C. § 119(e) of U.S. Provisional Patent Application No. 61/083,053,filed Jul. 23, 2008.

The present application is also a continuation-in-part of U.S. patentapplication Ser. No. 16/211,735, filed Dec. 6, 2018, which is acontinuation of U.S. application Ser. No. 15/167,710 filed May 27, 2016,now U.S. Pat. No. 10,182,870, which application is acontinuation-in-part of U.S. application Ser. No. 14/084,255 filed Nov.19, 2013, now U.S. Pat. No. 9,782,226, which application is acontinuation of U.S. application Ser. No. 13/086,275 (“the '275application”), filed Apr. 13, 2011, and titled “Preoperatively Planningan Arthroplasty Procedure and Generating a Corresponding PatientSpecific Arthroplasty Resection Guide,” now U.S. Pat. No. 8,617,171. The'275 application is a continuation-in-part (“CIP”) of U.S. patentapplication Ser. No. 12/760,388 (“the '388 application”), filed Apr. 14,2010, now U.S. Pat. No. 8,737,700. The '388 application is a CIPapplication of U.S. patent application Ser. No. 12/563,809 (“the '809application), filed Sep. 21, 2009, and titled “Arthroplasty System andRelated Methods,” now U.S. Pat. No. 8,545,509, which claims priority toU.S. patent application 61/102,692 (“the '692 application”), filed Oct.3, 2008, and titled “Arthroplasty System and Related Methods.” The '388application is also a CIP application of U.S. patent application Ser.No. 12/546,545 (“the 545 application”), filed Aug. 24, 2009, and titled“Arthroplasty System and Related Methods,” now U.S. Pat. No. 8,715,291,which claims priority to the '692 application. The '809 application isalso a CIP application of U.S. patent application Ser. No. 12/111,924(“the '924 application”), filed Apr. 29, 2008, and titled “Generation ofa Computerized Bone Model Representative of a Pre-Degenerated State andUseable in the Design and Manufacture of Arthroplasty Devices,” now U.S.Pat. No. 8,480,679. The '545 application is also a CIP application ofU.S. patent application Ser. No. 11/959,344 (“the '344 application),filed Dec. 18, 2007, and titled “System and Method for ManufacturingArthroplasty Jigs,” now U.S. Pat. No. 8,221,430. The '809 application isa CIP application of U.S. patent application Ser. No. 12/505,056 (“the'056 application”), filed Jul. 17, 2009, and titled “System and Methodfor Manufacturing Arthroplasty Jigs Having Improved Mating Accuracy,”now U.S. Pat. No. 8,777,875. The '056 application claims priority toU.S. patent application 61/083,053, filed Jul. 23, 2008, and titled“System and Method for Manufacturing Arthroplasty Jigs Having ImprovedMating Accuracy.” The '809 application is also a CIP application of the'344 application. The '388 application is also a CIP of the '344application. The '388 application is also a CIP of the '924 application.And the '388 application is also a CIP of the '056 application.

The present application claims priority to all of the above mentionedapplications and hereby incorporates by reference all of theabove-mentioned applications in their entireties into the presentapplication.

FIELD OF THE INVENTION

The present invention relates to image segmentation, morphing bonemodels to pre-degenerated states, and planning surgeries.

BACKGROUND OF THE INVENTION

Over time and through repeated use, bones and joints can become damagedor worn. For example, repetitive strain on bones and joints (e.g.,through athletic activity), traumatic events, and certain diseases(e.g., arthritis) can cause cartilage in joint areas, which normallyprovides a cushioning effect, to wear down. Cartilage wearing down canresult in fluid accumulating in the joint areas, pain, stiffness, anddecreased mobility.

Arthroplasty procedures can be used to repair damaged joints. During atypical arthroplasty procedure, an arthritic or otherwise dysfunctionaljoint can be remodeled or realigned, or an implant can be implanted intothe damaged region. Arthroplasty procedures may take place in any of anumber of different regions of the body, such as a knee, a hip, ashoulder, or an elbow.

One type of arthroplasty procedure is a total knee arthroplasty (“TKA”),in which a damaged knee joint is replaced with prosthetic implants. Theknee joint may have been damaged by, for example, arthritis (e.g.,severe osteoarthritis or degenerative arthritis), trauma, or a raredestructive joint disease. During a TKA procedure, a damaged portion inthe distal region of the femur may be removed and replaced with a metalshell, and a damaged portion in the proximal region of the tibia may beremoved and replaced with a channeled piece of plastic having a metalstem. In some TKA procedures, a plastic button may also be added underthe surface of the patella, depending on the condition of the patella.

Implants that are implanted into a damaged region may provide supportand structure to the damaged region, and may help to restore the damagedregion, thereby enhancing its functionality. Prior to implantation of animplant in a damaged region, the damaged region may be prepared toreceive the implant. For example, in a knee arthroplasty procedure, oneor more of the bones in the knee area, such as the femur and/or thetibia, may be treated (e.g., cut, drilled, reamed, and/or resurfaced) toprovide one or more surfaces that can align with the implant and therebyaccommodate the implant.

Accuracy in implant alignment is an important factor to the success of aTKA procedure. A one- to two-millimeter translational misalignment, or aone- to two-degree rotational misalignment, may result in imbalancedligaments, and may thereby significantly affect the outcome of the TKAprocedure. For example, implant misalignment may result in intolerablepost-surgery pain, and also may prevent the patient from having full legextension and stable leg flexion.

To achieve accurate implant alignment, prior to treating (e.g., cutting,drilling, reaming, and/or resurfacing) any regions of a bone, it isimportant to correctly determine the location at which the treatmentwill take place and how the treatment will be oriented. In some methods,an arthroplasty jig may be used to accurately position and orient afinishing instrument, such as a cutting, drilling, reaming, orresurfacing instrument on the regions of the bone. The arthroplasty jigmay, for example, include one or more apertures and/or slots that areconfigured to accept such an instrument.

A system and method has been developed for producing customizedarthroplasty jigs configured to allow a surgeon to accurately andquickly perform an arthroplasty procedure that restores thepre-deterioration alignment of the joint, thereby improving the successrate of such procedures. Specifically, the customized arthroplasty jigsare indexed such that they matingly receive the regions of the bone tobe subjected to a treatment (e.g., cutting, drilling, reaming, and/orresurfacing). The customized arthroplasty jigs are also indexed toprovide the proper location and orientation of the treatment relative tothe regions of the bone. The indexing aspect of the customizedarthroplasty jigs allows the treatment of the bone regions to be donequickly and with a high degree of accuracy that will allow the implantsto restore the patient's joint to a generally pre-deteriorated state.However, the system and method for generating the customized jigs oftenrelies on a human to “eyeball” bone models on a computer screen todetermine configurations needed for the generation of the customizedjigs. This “eyeballing” or manual manipulation of the bone modes on thecomputer screen is inefficient and unnecessarily raises the time,manpower and costs associated with producing the customized arthroplastyjigs. Furthermore, a less manual approach may improve the accuracy ofthe resulting jigs.

There is a need in the art for a system and method for reducing thelabor associated with generating customized arthroplasty jigs. There isalso a need in the art for a system and method for increasing theaccuracy of customized arthroplasty jigs.

SUMMARY

Aspects of the present disclosure may involve a method for planning anarthroplasty procedure on a patient bone. The method may includeaccessing generic bone data stored in a memory of a computer, using thecomputer to generate modified bone data by modifying the generic bonedata according to medical imaging data of the patient bone, using thecomputer to derive a location of non-bone tissue data relative to themodified bone data, and superimposing implant data and the modified bonedata in defining a resection of an arthroplasty target region of thepatient bone.

In certain instances, the non-bone tissue data may include a contour ofthe non-bone tissue data.

In certain instances, the non-bone tissue data pertains to cartilage.

In certain instances, the non-bone tissue data may include modifiednon-bone tissue data that may be computer generated by accessing genericnon-bone tissue data stored in the memory and using the computer tomodify the generic non-bone tissue data according to the medical imagingdata of the patient bone.

In certain instances, the modified non-bone tissue data may include acontour of the non-bone tissue data.

In certain instances, the modified non-bone tissue data pertains tocartilage.

In certain instances, the contour of the non-bone tissue data may beused in registering the resection with the patient bone.

Aspects of the present disclosure may involve a surgical method andfurther may include resecting the resection into the patient bone.

In certain instances, the contour of the non-bone tissue data may beused in defining a registration surface of an arthroplasty jig, theregistration surface registering the arthroplasty jig with the patientbone when the arthroplasty jig may be used to guide the resection in thearthroplasty target region of the patient bone.

Aspects of the present disclosure may involve a manufacturing method andfurther may include manufacturing the arthroplasty jig to may includethe registration surface and a resection guide capable of guiding theresection when the registration surface interdigitates with the patientbone.

In certain instances, the method further may include comparing themodified bone data to candidate implant models stored in the memory ofthe computer.

In certain instances, the method further may include recommending animplant model based on the comparison of the modified bone data to thecandidate implant models.

In certain instances, the method further may include presenting thedefined resection to a surgeon for review.

Aspects of the present disclosure may involve a method for planning anarthroplasty procedure on a joint region of a patient bone. The methodmay include constructing a virtual bone model of the joint region of thepatient bone, the virtual bone model may include a contour of softtissue and a bone surface, determining a location and configuration ofthe soft tissue relative to the bone surface of the virtual bone model,identifying a registration surface including at least part of thelocation and configuration of the soft tissue, superimposing a virtualimplant model over the bone surface of the virtual bone model,determining a resection relative to the bone surface of the virtual bonemodel based on the superimposing, the resection being adapted tofacilitate an implant being implanted on the patient bone as part of thearthroplasty procedure, the implant corresponding to the virtual implantmodel, and referencing the resection to the registration surface.

In certain instances, the soft tissue may include cartilage.

In certain instances, the virtual bone model may be computer generatedby accessing a generic bone model stored in a memory and using acomputer to modify the generic bone model according to medical imagingdata of the joint region of the patient bone.

In certain instances, the method further may include comparing thevirtual bone model to candidate implant models stored in a memory of acomputer.

In certain instances, the method further may include recommending animplant model based on the comparison of the virtual bone model to thecandidate implant models.

In certain instances, the method further may include presenting theresection to a surgeon for review.

In certain instances, the virtual bone model may include a bone andcartilage model and a bone-only model.

Aspects of the present disclosure may involve a surgical method whichfurther may include resecting the resection into the patient bone.

Aspects of the present disclosure may involve a manufacturing method andfurther may include manufacturing an arthroplasty jig to include amating surface and a resection guide, the mating surface adapted tointerdigitate with the registration surface, and the resection guidecapable of guiding the resection when the mating surface interdigitateswith the patient bone.

While multiple embodiments are disclosed, still other embodiments of thepresent invention will become apparent to those skilled in the art fromthe following detailed description, which shows and describesillustrative embodiments of the invention. As will be realized, theinvention is capable of modifications in various aspects, all withoutdeparting from the spirit and scope of the present invention.Accordingly, the drawings and detailed description are to be regarded asillustrative in nature and not restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a schematic diagram of a system for employing the automatedjig production method disclosed herein.

FIGS. 1B-1E are flow chart diagrams outlining the jig production methoddisclosed herein.

FIGS. 1F and 1G are, respectively, bottom and top perspective views ofan example customized arthroplasty femur jig.

FIGS. 1H and 1I are, respectively, bottom and top perspective views ofan example customized arthroplasty tibia jig.

FIG. 2A is a sagittal plane image slice depicting a femur and tibia andneighboring tissue regions with similar image intensity.

FIG. 2B is a sagittal plane image slice depicting a region extendinginto the slice from an adjacent image slice.

FIG. 2C is a sagittal plane image slice depicting a region of a femurthat is approximately tangent to the image slice.

FIG. 3A is a sagittal plane image slice depicting an intensity gradientacross the slice.

FIG. 3B is a sagittal plane image slice depicting another intensitygradient across the slice.

FIG. 3C is a sagittal plane image slice depicting another intensitygradient across the slice.

FIG. 4A depicts a sagittal plane image slice with a high noise level.

FIG. 4B depicts a sagittal plane image slice with a low noise level.

FIG. 5 is a sagittal plane image slice of a femur and tibia depictingregions where good definition may be needed during automaticsegmentation of the femur and tibia.

FIG. 6 depicts a flowchart illustrating one method for automaticsegmentation of an image modality scan of a patient's knee joint.

FIG. 7A is a sagittal plane image slice of a segmented femur.

FIG. 7B is a sagittal plane image slice of a segmented femur and tibia.

FIG. 7C is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7D is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7E is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7F is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7G is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7H is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7I is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7J is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7K is another sagittal plane image slice of a segmented femur andtibia.

FIG. 8 is a sagittal plane image slice depicting automatically generatedslice curves of a femur and a tibia.

FIG. 9 depicts a 3D mesh geometry of a femur.

FIG. 10 depicts a 3D mesh geometry of a tibia.

FIG. 11 depicts a flowchart illustrating one method for generating agolden template.

FIG. 12A is a sagittal plane image slice depicting a contour curveoutlining a golden tibia region, a contour curve outlining a grown tibiaregion and a contour curve outlining a boundary golden tibia region.

FIG. 12B is a sagittal plane image slice depicting a contour curveoutlining a golden femur region, a contour curve outlining a grown femurregion and a contour curve outlining a boundary golden femur region.

FIG. 13A depicts a golden tibia 3D mesh.

FIG. 13B depicts a golden femur 3D mesh.

FIG. 14A is a sagittal plane image slice depicting anchor segmentationregions of a tibia.

FIG. 14B is a sagittal plane image slice depicting anchor segmentationregions of a femur.

FIG. 15A is a 3D mesh geometry depicting the anchor segmentation mesh,the InDark-OutLight anchor mesh, the InLight-OutDark anchor mesh, andthe Dark-In-Light anchor mesh of a tibia.

FIG. 15B is a 3D mesh geometry depicting the anchor segmentation mesh,the InDark-OutLight anchor mesh and the InLight-OutDark anchor mesh of afemur.

FIG. 16 depicts a flowchart illustrating one method for performingautomatic segmentation of scan data using golden template registration.

FIG. 17 depicts a flowchart illustrating one method for mapping thesegmented golden femur template regions into the target scan data usingimage registration techniques.

FIG. 18 depicts a registration framework that may be employed by oneembodiment.

FIG. 19 depicts a flowchart illustrating one method for mapping thesegmented golden tibia template regions into the target scan data usingimage registration techniques.

FIG. 20 depicts a flowchart illustrating one method for computing ametric for the registration framework of FIG. 18.

FIG. 21 depicts a flowchart illustrating one method for refining theregistration results using anchor segmentation and anchor regions.

FIG. 22 depicts a set of randomly generated light sample points and darksample points of a tibia.

FIG. 23 depicts a flowchart illustrating one method for generatingspline curves to outline features of interest in each target MRI slice.

FIG. 24 depicts a polyline curve with n vertices.

FIG. 25 depicts a flowchart illustrating one method for adjustingsegments.

FIG. 26 is a sagittal plane image slice depicting a contour curve withcontrol points outlining a femur with superimposed contour curves of thefemur from adjacent image slices.

FIG. 27 depicts a 3D slice visualization of a femur showing the voxelsinside of the spline curves.

FIG. 28 is a diagram depicting types of data employed in the imagesegmentation algorithm that uses landmarks.

FIG. 29 is a flowchart illustrating the overall process for generating agolden femur model of FIG. 28.

FIG. 30 is an image slice of the representative femur to be used togenerate a golden femur mesh.

FIG. 31A is an isometric view of a closed golden femur mesh.

FIG. 31B is an isometric view of an open golden femur mesh created fromthe closed golden femur mesh of FIG. 31A.

FIG. 31C is the open femur mesh of FIG. 31B with regions of a differentprecision indicated.

FIGS. 32A-32B are isometric views of an open golden tibia mesh withregions of a different precision indicated.

FIG. 33 is a flow chart illustrating an alternative method of segmentingan image slice, the alternative method employing landmarks.

FIG. 34 is a flow chart illustrating the process involved in operation“position landmarks” of the flow chart of FIG. 33.

FIGS. 35A-35H are a series of sagittal image slices wherein landmarkshave been placed according the process of FIG. 34.

FIG. 36 is a flowchart illustrating the process of segmenting the targetimages that were provided with landmarks in operation “positionlandmarks” of the flow chart of FIG. 33.

FIG. 37 is a flowchart illustrating the process of operation “DeformGolden Femur Mesh” of FIG. 36, the process including mapping the goldenfemur mesh into the target scan using registration techniques.

FIG. 38A is a flowchart illustrating the process of operation “DetectAppropriate Image Edges” of FIG. 37.

FIG. 38B is an image slice with a contour line representing theapproximate segmentation mesh surface found in operation 770 c of FIG.37, the vectors showing the gradient of the signed distance for thecontour.

FIG. 38C is an enlarged view of the area in FIG. 38B enclosed by thesquare 1002, the vectors showing the computed gradient of the targetimage.

FIG. 38D is the same view as FIG. 38C, except the vectors of FIGS. 38Band 38C are superimposed.

FIG. 39 is a flowchart illustrating the process of operation “ModifySplines” of FIG.

FIG. 40 is an image slice with a spline being modified according to theoperations of the flow chart of FIG. 39.

FIG. 41A is an anterior-posterior image slice of the damaged lower orknee joint end of the patient's femur, wherein the image slice includesan open-loop contour line segment corresponding to the targeted regionof the damaged lower end.

FIG. 41B is a plurality of image slices with their respective open-loopcontour line segments, the open-loop contour line segments beingaccumulated to generate the 3D model of the targeted region.

FIG. 41C is a 3D model of the targeted region of the damaged lower endas generated using the open-loop contour line segments depicted in FIG.41B.

FIG. 41D is an anterior-posterior image slice of the damaged lower orknee joint end of the patient's femur, wherein the image slice includesa closed-loop contour line corresponding to the femur lower end,including the targeted region.

FIG. 41E is a plurality of image slices with their respectiveclosed-loop contour line segments, the closed-loop contour lines beingaccumulated to generate the 3D model of the femur lower end, includingthe targeted region.

FIG. 41F is a 3D model of the femur lower end, including the targetedregion, as generated using the closed-loop contour lines depicted inFIG. 41B.

FIG. 41G is a flow chart illustrating an overview of the method ofproducing a femur jig.

FIG. 41H is a top perspective view of a left femoral cutting jig blankhaving predetermined dimensions.

FIG. 41I is a bottom perspective view of the jig blank depicted in FIG.41H.

FIG. 41J is plan view of an exterior side or portion of the jig blankdepicted in FIG. 41H.

FIG. 41K is a plurality of available sizes of left femur jig blanks,each depicted in the same view as shown in FIG. 41J.

FIG. 41L is a plurality of available sizes of right femur jig blanks,each depicted in the same view as shown in FIG. 41J.

FIG. 42A is an axial view of the 3D surface model or arthritic model ofthe patient's left femur as viewed in a direction extending distal toproximal.

FIG. 42B depicts the selected model jig blank of FIG. 3C superimposed onthe model femur lower end of FIG. 42A.

FIG. 42C is an example scatter plot for selecting from a plurality ofcandidate jig blanks sizes a jig blank size appropriate for the lowerend of the patient's femur.

FIG. 42D is a flow diagram illustrating an embodiment of a process ofselecting an appropriately sized jig blank.

FIG. 42E is an exterior perspective view of a femur jig blank exteriorsurface model.

FIG. 42F is an interior perspective view of the femur jig blank exteriorsurface model of FIG. 42E.

FIG. 42G is a perspective view of the extracted jig blank exteriorsurface model being combined with the extracted femur surface model.

FIG. 42H is a perspective view of the extracted jig blank exteriorsurface model combined with the extracted femur surface model.

FIG. 42I is a cross section of the combined jig blank exterior surfacemodel and the femur surface model as taken along section line 42I-421 inFIG. 42H.

FIG. 42J is an exterior perspective view of the resulting femur jigmodel.

FIG. 42K is an interior perspective view of the femur jig model of FIG.42J.

FIG. 42L illustrates a perspective view of the integrated jig modelmating with the “arthritic model”.

FIG. 43A is an anterior-posterior image slice of the damaged upper orknee joint end of the patient's tibia, wherein the image slice includesan open-loop contour line segment corresponding to the target area ofthe damaged upper end.

FIG. 43B is a plurality of image slices with their respective open-loopcontour line segments, the open-loop contour line segments beingaccumulated to generate the 3D model of the target area.

FIG. 43C is a 3D model of the target area of the damaged upper end asgenerated using the open-loop contour line segments depicted in FIG.43B.

FIG. 43D is a top perspective view of a right tibia cutting jig blankhaving predetermined dimensions.

FIG. 43E is a bottom perspective view of the jig blank depicted in FIG.43D.

FIG. 43F is plan view of an exterior side or portion of the jig blankdepicted in FIG. 43D.

FIG. 43G is a plurality of available sizes of right tibia jig blanks,each depicted in the same view as shown in FIG. 43F.

FIG. 43H is a plurality of available sizes of left tibia jig blanks,each depicted in the same view as shown in FIG. 43F.

FIG. 43I is an axial view of the 3D surface model or arthritic model ofthe patient's right tibia as viewed in a direction extending proximal todistal.

FIG. 43J depicts the selected model jig blank of FIG. 43F superimposedon the model tibia upper end of FIG. 43I.

FIG. 43K is an example scatter plot for selecting from a plurality ofcandidate jig blanks sizes a jig blank size appropriate for the upperend of the patient's tibia.

FIG. 43L is a flow diagram illustrating an embodiment of a process ofselecting an appropriately sized jig blank.

FIG. 44A is an exterior perspective view of a tibia jig blank exteriorsurface model.

FIG. 44B is an interior perspective view of the tibia jig blank exteriorsurface model of FIG. 44A.

FIG. 44C is a perspective view of the extracted jig blank exteriorsurface model being combined with the extracted tibia surface model.

FIGS. 44D-44F are perspective views of the extracted jig blank exteriorsurface model combined with the extracted tibia surface model.

FIG. 44G is an exterior perspective view of the resulting tibia jigmodel.

FIG. 44H is an interior perspective view of the tibia jig model of FIG.44G.

FIG. 44I illustrates a perspective view of the integrated jig modelmating with the “arthritic model”.

FIG. 45A illustrates the distal axial view of the 3D model of thepatient's femur shown in FIG. 42A with the contour lines of the imageslices shown and spaced apart by the thickness D_(T) of the slices.

FIG. 45B represents a coronal view of a 3D model of the patient's femurwith the contour lines of the image slices shown and spaced apart by thethickness D_(T) of the slices.

FIG. 45C illustrates an example sagittal view of compiled contour linesof successive sagittal 2D MRI images based on the slices shown in FIGS.45A-B with a slice thickness D_(T) of 2 mm.

FIG. 45D illustrates an example contour line of one of the contour linesdepicted in FIGS. 45A-45C, wherein the contour line is depicted in asagittal view and is associated with an image slice of the femoralcondyle.

FIG. 45E represents an example overestimation algorithm that may be usedto identify and adjust for irregular contour line regions when formingthe 3D model.

FIG. 45F depicts implementing an example analysis scheme (according toblock 2506) on the irregular contour line region 2402B of FIG. 45D.

FIG. 45G depicts the irregular region 2402B from FIG. 45F including aproposed area of overestimation, wherein an overestimation procedurecreates an adjusted contour line and positionally deviates the adjustedcontour line from the original surface profile contour line.

FIG. 45H illustrates the example analysis scheme according to thealgorithm of FIG. 45E implemented on the irregular region 2402C fromFIG. 45D where an irregular surface of the condylar contour is observed.

FIG. 45I depicts the irregular region 2402C from FIG. 45H including aproposed area of overestimation indicated by the dashed line areas2902A-B.

FIG. 45J is similar to FIG. 45I, except depicting a tool with a largerdiameter.

FIG. 45K is similar to FIG. 45J, except depicting a tool with a largerdiameter.

FIG. 45L depicts the irregular region 2402D from FIG. 45D including aproposed area of overestimation indicated by the dashed line.

FIG. 45M shows an analysis of the regular region 2402A from FIG. 45D.

FIG. 45N is a diagrammatic sagittal-coronal-distal isometric view ofthree contour lines of three adjacent image slices depicting angularrelationships that may be used to determine whether portions of the oneor more contour lines may be employed to generate 3D computer models.

FIGS. 45O-T are example right triangles that may be used for determiningthe angular deviation θ between corresponding coordinate points ofcontour lines of adjacent image slices per block 2514 of FIG. 45E.

FIG. 46A depicts portions of contour lines n^(th), n^(th+1), n^(th+2),n^(th+3) and n^(th+4) in a sagittal view similar to that of FIG. 45C.

FIG. 46B is a bone surface contour line and a linear interpolation bonesurface contour line as viewed along a section line 33B-33B transverseto image slices containing the contour lines n^(th), n^(th+1), n^(th+2),n^(th+3) and n^(th+4) of FIG. 46A.

FIG. 46C depicts portions of contour lines n^(th), n^(th+1), n^(th+2),n^(th+3) and n^(th+4) in a sagittal view similar to that of FIG. 45C.

FIG. 46D is a bone surface contour line and a linear interpolation bonesurface contour line as viewed along a section line 46D-46D transverseto image slices containing the contour lines n^(th), n^(th+1), n^(th+2),n^(th+3) and n^(th+4) of FIG. 46C.

FIG. 46E depicts portions of contour lines n^(th), n^(th+1), n^(th+2),n^(th+3) and n^(th+4) in a sagittal view similar to that of FIG. 45C.

FIG. 46F is a bone surface contour line and a linear interpolation bonesurface contour line as viewed along a section line 46F-46F transverseto image slices containing the contour lines n^(th), n^(th+1), n^(th+2),n^(th+3) and n^(th+4) of FIG. 46E.

FIG. 46G is a distal view similar to that of FIG. 42A depicting contourlines produced by imaging the right femur at an image spacing D_(T) of,for example, 2 mm.

FIGS. 46H-46K are sagittal views of the contour lines of respectiveregions of FIG. 46G.

FIG. 47A is distal-sagittal isometric view of a femoral distal end.

FIG. 47B is a bottom perspective view of an example customizedarthroplasty femur jig that has been generated via the overestimationprocess disclosed herein.

FIG. 47C is an anterior-posterior cross-section of the femur jig of FIG.47B mounted on the femur distal end of FIG. 47A.

FIG. 47D is a coronal view of the anterior side of the femoral distalend.

FIG. 47E depicts closed-loop contour lines that are image segmented fromimage slices, wherein the contour lines outline the cortical bonesurface of the lower end of the femur.

FIG. 48A illustrates the proximal axial view of the 3D model of thepatient's tibia shown in FIG. 15 with the contour lines of the imageslices shown and spaced apart by the thickness D_(T) of the slices.

FIG. 48B represents a coronal view of a 3D model of the patient's tibiawith the contour lines of the image slices shown and spaced apart by thethickness D_(T) of the slices.

FIG. 48C illustrates an example sagittal view of compiled contour linesof successive sagittal 2D MRI images based on the slices shown in FIGS.41A-B with a slice thickness D_(T) of 2 mm.

FIG. 48D illustrates an example contour line of one of the contour linesdepicted in FIGS. 48A-48C, wherein the contour line is depicted in asagittal view and is associated with an image slice of the tibiaplateau.

FIG. 48E depicts implementing an example analysis scheme (according toblock 2506) on the irregular contour line region 4302B of FIG. 48D.

FIG. 48F depicts the irregular region 4302B from FIG. 48E including aproposed area of overestimation, wherein an overestimation procedurecreates an adjusted contour line and positionally deviates the adjustedcontour line from the original surface profile contour line.

FIGS. 48G and 48H show an analysis of the regular regions 4302A and4302C from FIG. 48D.

FIG. 48I is a distal view similar to that of FIG. 43I depicting contourlines produced by imaging the left tibia at an image spacing D_(T) of,for example, 2 mm.

FIGS. 48J-48M are sagittal views of the contour lines of respectiveregions of FIG. 48I.

FIG. 49A is distal-sagittal isometric view of a tibial proximal end.

FIGS. 49B-49C are, respectively, top and bottom perspective views of anexample customized arthroplasty tibia jig that has been generated viathe overestimation process disclosed herein.

FIG. 49D is an anterior-posterior cross-section of the tibia jig ofFIGS. 49B-C mounted on the tibia proximal end of FIG. 49A.

FIG. 49E is a coronal view of the anterior side of the tibial proximalend.

FIG. 49F depicts closed-loop contour lines that are image segmented fromimage slices, wherein the contour lines outline the cortical bonesurface of the upper end of the tibia.

FIG. 49G is an anterior isometric view of the femur distal end.

FIG. 49H is an anterior isometric view of the tibia proximal end.

FIGS. 50A-50E are flow chart diagrams outlining the jig productionmethod disclosed herein.

FIGS. 51A and 51B are, respectively, bottom and top perspective views ofan example customized arthroplasty femur jig.

FIGS. 51C and 51D are, respectively, top/posterior and bottom/anteriorperspective views of an example customized arthroplasty tibia jig.

FIG. 52A is an isometric view of a 3D computer model of a femur lowerend and a 3D computer model of a tibia upper end in position relative toeach to form a knee joint and representative of the femur and tibia in anon-degenerated state.

FIG. 52B is an isometric view of a 3D computer model of a femur implantand a 3D computer model of a tibia implant in position relative to eachto form an artificial knee joint.

FIG. 53 is a perspective view of the distal end of 3D model of the femurwherein the femur reference data is shown.

FIG. 54A is a sagittal view of a femur illustrating the orders andorientations of imaging slices utilized in the femur POP.

FIG. 54B depicts axial imaging slices taken along section lines of thefemur of FIG. 54A.

FIG. 54C depicts coronal imaging slices taken along section lines of thefemur of FIG. 54A.

FIG. 55A is an axial imaging slice taken along section lines of thefemur of FIG. 54A, wherein the distal reference points are shown.

FIG. 55B is an axial imaging slice taken along section lines of thefemur of FIG. 54A, wherein the trochlear groove bisector line is shown.

FIG. 55C is an axial imaging slice taken along section lines of thefemur of FIG. 54A, wherein the femur reference data is shown.

FIG. 55D is the axial imaging slices taken along section lines of thefemur in FIG. 54A.

FIG. 56A is a coronal slice taken along section lines of the femur ofFIG. 54A, wherein the femur reference data is shown

FIG. 56B is the coronal imaging slices taken along section lines of thefemur in FIG. 54A.

FIG. 56C is a sagittal imaging slice of the femur in FIG. 54A.

FIG. 56D is an axial imaging slice taken along section lines of thefemur of FIG. 54A, wherein the femur reference data is shown.

FIG. 56E is a coronal imaging slice taken along section lines of thefemur of FIG. 54A, wherein the femur reference data is shown.

FIG. 57 is a posterior view of a 3D model of a distal femur.

FIG. 58 depicts a y-z coordinate system wherein the femur reference datais shown.

FIG. 59 is a perspective view of a femoral implant model, wherein thefemur implant reference data is shown.

FIG. 60 is another perspective view of a femoral implant model, whereinthe femur implant reference data is shown.

FIG. 61 is a y-z coordinate system wherein some of the femur implantreference data is shown.

FIG. 62 is an x-y-z coordinate system wherein the femur implantreference data is shown.

FIG. 63A shows the femoral condyle and the proximal tibia of the knee ina sagittal view MRI image slice.

FIG. 63B is a coronal view of a knee model in extension.

FIGS. 63C and 63D illustrate MRI segmentation slices for joint lineassessment.

FIG. 63E is a flow chart illustrating the method for determiningcartilage thickness used to determine proper joint line.

FIG. 63F illustrates a MRI segmentation slice for joint line assessment.

FIGS. 63G and 63H illustrate coronal views of the bone images in theiralignment relative to each as a result of OA.

FIG. 63I illustrates a coronal view of the bone images with a restoredgap Gp3.

FIG. 63J is a coronal view of bone images oriented relative to eachother in a deteriorated state orientation.

FIG. 64 is a 3D coordinate system wherein the femur reference data isshown.

FIG. 65 is a y-z plane wherein the joint compensation points are shown.

FIG. 66 illustrates the implant model 34′ placed onto the samecoordinate plane with the femur reference data.

FIG. 67A is a plan view of the joint side of the femur implant modeldepicted in FIG. 52B.

FIG. 67B is an axial end view of the femur lower end of the femur bonemodel depicted in FIG. 52A.

FIG. 67C illustrates the implant extents iAP and iML and the femurextents bAP, bML as they may be aligned for proper implant placement.

FIG. 68A shows the most medial edge of the femur in a 2D sagittalimaging slice.

FIG. 68B, illustrates the most lateral edge of the femur in a 2Dsagittal imaging slice.

FIG. 68C is a 2D imaging slice in coronal view showing the medial andlateral edges.

FIG. 69A is a candidate implant model mapped onto a y-z plane.

FIG. 69B is the silhouette curve of the articular surface of thecandidate implant model.

FIG. 69C is the silhouette curve of the candidate implant model alignedwith the joint spacing compensation points D_(1J)D_(2J) andP_(1J)P_(2J).

FIG. 70A illustrates a sagittal imaging slice of a distal femur with animplant model.

FIG. 70B depicts a femur implant model wherein the flange point on theimplant is shown.

FIG. 70C shows an imaging slice of the distal femur in the sagittalview, wherein the inflection point located on the anterior shaft of thespline is shown.

FIG. 70D illustrates the 2D implant model properly positioned on the 2Dfemur image, as depicted in a sagittal view.

FIG. 71A depicts an implant model that is improperly aligned on a 2Dfemur image, as depicted in a sagittal view.

FIG. 71B illustrates the implant positioned on a femur transform whereina femur cut plane is shown, as depicted in a sagittal view.

FIG. 72 is a top view of the tibia plateaus of a tibia bone image ormodel.

FIG. 73A is a sagittal cross section through a lateral tibia plateau ofthe 2D tibia bone model or image.

FIG. 73B is a sagittal cross section through a medial tibia plateau ofthe 2D tibia bone model or image.

FIG. 73C depicts a sagittal cross section through an undamaged or littledamaged medial tibia plateau of the 2D tibia model, wherein osteophytesare also shown.

FIG. 73D is a sagittal cross section through a damaged lateral tibiaplateau of the 2D tibia model.

FIG. 74A is a coronal 2D imaging slice of the tibia.

FIG. 74B is an axial 2D imaging slice of the tibia.

FIG. 75A depicts the tibia reference data on an x-y coordinate system.

FIG. 75B depicts the tibia reference data on a proximal end of the tibiato aid in the selection of an appropriate tibia implant.

FIG. 76A is a 2D sagittal imaging slice of the tibia wherein asegmentation spline with an AP extant is shown.

FIG. 76B is an axial end view of the tibia upper end of the tibia bonemodel depicted in FIG. 52A.

FIG. 76C is a plan view of the joint side of the tibia implant modeldepicted in FIG. 52B.

FIG. 77 is a top isometric view of the tibia plateaus of a tibia implantmodel.

FIG. 78A is an isometric view of the 3D tibia bone model showing thesurgical cut plane SCP design.

FIGS. 78B and 78C are sagittal MRI views of the surgical tibia cut planeSCP design with the posterior cruciate ligament PCL.

FIG. 79A is an isometric view of the tibia implant wherein a cut planeis shown.

FIG. 79B is a top axial view of the implant superimposed on the tibiareference data.

FIG. 79C is an axial view of the tibial implant aligned with the tibiareference data.

FIG. 79D is a MRI imaging slice of the medial portion of the proximaltibia and indicates the establishment of landmarks for the tibia POPdesign, as depicted in a sagittal view.

FIG. 79E is a MRI imaging slice of the lateral portion of the proximaltibia, as depicted in a sagittal view.

FIG. 79F is an isometric view of the 3D model of the tibia implant andthe cut plane.

FIGS. 80A-80B are sagittal views of a 2D imaging slice of the femurwherein the 2D computer generated implant models are also shown.

FIG. 80C is a sagittal view of a 2D imaging slice of the tibia whereinthe 2D computer generated implant model is also shown.

FIGS. 81A-81C are various views of the 2D implant models superimposed onthe 2D bone models.

FIG. 81D is a coronal view of the 2D bone models.

FIGS. 81E-81G are various views of the 2D implant models superimposed onthe 2D bone models.

FIG. 82A is a medial view of the 3D bone models.

FIG. 82B is a medial view of the 3D implant models

FIG. 82C is a medial view of the 3D implant models superimposed on the3D bone models.

DETAILED DESCRIPTION

Disclosed herein are customized arthroplasty jigs 2 and systems 4 for,and methods of, producing such jigs 2. The jigs 2 are customized to fitspecific bone surfaces of specific patients. Depending on the embodimentand to a greater or lesser extent, the jigs 2 are automatically plannedand generated and may be similar to those disclosed in these three U.S.Patent Applications: U.S. patent application Ser. No. 11/656,323 to Parket al., titled “Arthroplasty Devices and Related Methods” and filed Jan.19, 2007; U.S. patent application Ser. No. 10/146,862 to Park et al.,titled “Improved Total Joint Arthroplasty System” and filed May 15,2002; and U.S. patent Ser. No. 11/642,385 to Park et al., titled“Arthroplasty Devices and Related Methods” and filed Dec. 19, 2006. Thedisclosures of these three U.S. Patent Applications are incorporated byreference in their entireties into this Detailed Description.

As an overview, Section I. of the present disclosure provides adescription of systems and methods of manufacturing custom arthroplastycutting guides. Section II. of the present disclosure provides anoverview of exemplary segmentation processes performed on medicalimages, and the generation of bone models representing bones of a jointin a deteriorated state. Section III. of the present disclosuredescribes an overestimation process where certain areas of the bone inthe medical images are identified for generating mating jig surfaces,and certain areas of the bone in the medical images are identified asnon-mating areas between a jig and the bone surface. And Section IV. ofthe present disclosure provides an overview of the pre-operativesurgical planning process that may take place on the patient's imagedata.

I. Overview of System and Method for Manufacturing CustomizedArthroplasty Cutting Jigs

For an overview discussion of the systems 4 for, and methods of,producing the customized arthroplasty jigs 2, reference is made to FIGS.1A-1E. FIG. 1A is a schematic diagram of a system 4 for employing theautomated jig production method disclosed herein. FIGS. 1B-1E are flowchart diagrams outlining the jig production method disclosed herein. Thefollowing overview discussion can be broken down into three sections.

The first section, which is discussed with respect to FIG. 1A and[blocks 100-125] of FIGS. 1B-1E, pertains to an example method ofdetermining, in a three-dimensional (“3D”) computer model environment,saw cut and drill hole locations 30, 32 relative to 3D computer modelsthat are termed restored bone models 28 (also referenced as “planningmodels” throughout this submission.) In some embodiments, the resulting“saw cut and drill hole data” 44 is referenced to the restored bonemodels 28 to provide saw cuts and drill holes that will allowarthroplasty implants to restore the patient's joint to itspre-degenerated state. In other words, in some embodiments, thepatient's joint may be restored to its natural alignment, whethervalgus, varus or neutral.

While many of the embodiments disclosed herein are discussed withrespect to allowing the arthroplasty implants to restore the patient'sjoint to its pre-degenerated or natural alignment state, many of theconcepts disclosed herein may be applied to embodiments wherein thearthroplasty implants restore the patient's joint to a zero mechanicalaxis alignment such that the patient's knee joint ends up being neutral,regardless of whether the patient's predegenerated condition was varus,valgus or neutral. For example, as disclosed in U.S. patent applicationSer. No. 12/760,388 to Park et al., titled “Preoperatively Planning AnArthroplasty Procedure And Generating A Corresponding Patient SpecificArthroplasty Resection Guide”, filed Apr. 14, 2010, and incorporated byreference into this Detailed Description in its entirety, the system 4for producing the customized arthroplasty jigs 2 may be such that thesystem initially generates the preoperative planning (“POP”) associatedwith the jig in the context of the POP resulting in the patient's kneebeing restored to its natural alignment. Such a natural alignment POP isprovided to the physician, and the physician determines if the POPshould result in (1) natural alignment, (2) mechanical alignment, or (3)something between (1) and (2). The POP is then adjusted according to thephysician's determination, the resulting jig 2 being configured suchthat the arthroplasty implants will restore the patient's joint to (1),(2) or (3), depending on whether the physician elected (1), (2) or (3),respectively. Accordingly, this disclosure should not be limited tomethods resulting in natural alignment only, but should, whereappropriate, be considered as applicable to methods resulting in naturalalignment, zero mechanical axis alignment or an alignment somewherebetween natural and zero mechanical axis alignment.

The second section, which is discussed with respect to FIG. 1A and[blocks 100-105 and 130-145] of FIGS. 1B-1E, pertains to an examplemethod of importing into 3D computer generated jig models 38 3D computergenerated surface models 40 of arthroplasty target areas 42 of 3Dcomputer generated arthritic models 36 of the patient's joint bones. Theresulting “jig data” 46 is used to produce a jig customized to matinglyreceive the arthroplasty target areas of the respective bones of thepatient's joint.

The third section, which is discussed with respect to FIG. 1A and[blocks 150-165] of FIG. 1E, pertains to a method of combining orintegrating the “saw cut and drill hole data” 44 with the “jig data” 46to result in “integrated jig data” 48. The “integrated jig data” 48 isprovided to the CNC machine 10 for the production of customizedarthroplasty jigs 2 from jig blanks 50 provided to the CNC machine 10.The resulting customized arthroplasty jigs 2 include saw cut slots anddrill holes positioned in the jigs 2 such that when the jigs 2 matinglyreceive the arthroplasty target areas of the patient's bones, the cutslots and drill holes facilitate preparing the arthroplasty target areasin a manner that allows the arthroplasty joint implants to generallyrestore the patient's joint line to its pre-degenerated state.

As shown in FIG. 1A, the system 4 includes one or more computers 6having a CPU 7, a monitor or screen 9 and an operator interface controls11. The computer 6 is linked to a medical imaging system 8, such as a CTor MRI machine 8, and a computer controlled machining system 10, such asa CNC milling machine 10.

In another embodiment, rather than using a single computer for the wholeprocess, multiple computers can perform separate steps of the overallprocess, with each respective step managed by a respective technicianskilled in that particular aspect of the overall process. The datagenerated in one process step on one computer can be then transferredfor the next process step to another computer, for instance via anetwork connection.

As indicated in FIG. 1A, a patient 12 has a joint 14 (e.g., a knee,elbow, ankle, wrist, hip, shoulder, skull/vertebrae orvertebrae/vertebrae interface, etc.) to be replaced. The patient 12 hasthe joint 14 scanned in the imaging machine 8. The imaging machine 8makes a plurality of scans of the joint 14, wherein each scan pertainsto a thin slice of the joint 14.

As can be understood from FIG. 1B, the plurality of scans is used togenerate a plurality of two-dimensional (“2D”) images 16 of the joint 14[block 100]. Where, for example, the joint 14 is a knee 14, the 2Dimages will be of the femur 18 and tibia 20. The imaging may beperformed via CT or MRI. In one embodiment employing MRI, the imagingprocess may be as disclosed in U.S. patent application Ser. No.11/946,002 to Park, which is entitled “Generating MRI Images Usable ForThe Creation Of 3D Bone Models Employed To Make Customized ArthroplastyJigs,” was filed Nov. 27, 2007 and is incorporated by reference in itsentirety into this Detailed Description.

As can be understood from FIG. 1A, the 2D images are sent to thecomputer 6 for creating computer generated 3D models. As indicated inFIG. 1B, in one embodiment, point P is identified in the 2D images 16[block 105]. In one embodiment, as indicated in [block 105] of FIG. 1A,point P may be at the approximate medial-lateral and anterior-posteriorcenter of the patient's joint 14. In other embodiments, point P may beat any other location in the 2D images 16, including anywhere on, nearor away from the bones 18, 20 or the joint 14 formed by the bones 18,20.

As described later in this overview, point P may be used to locate thecomputer generated 3D models 22, 28, 36 created from the 2D images 16and to integrate information generated via the 3D models. Depending onthe embodiment, point P, which serves as a position and/or orientationreference, may be a single point, two points, three points, a point plusa plane, a vector, etc., so long as the reference P can be used toposition and/or orient the 3D models 22, 28, 36 generated via the 2Dimages 16.

As discussed in detail below, the 2D images 16 are segmented along boneboundaries to create bone contour lines. Also, the 2D images 16 aresegmented along bone and cartilage boundaries to create bone andcartilage lines.

As shown in FIG. 1C, the segmented 2D images 16 (i.e., bone contourlines) are employed to create computer generated 3D bone-only (i.e.,“bone models”) 22 of the bones 18, 20 forming the patient's joint 14[block 110]. The bone models 22 are located such that point P is atcoordinates (X_(P), Y_(P), Z_(P)) relative to an origin (X₀, Y₀, Z₀) ofan X-Y-Z coordinate system [block 110]. The bone models 22 depict thebones 18, 20 in the present deteriorated condition with their respectivedegenerated joint surfaces 24, 26, which may be a result ofosteoarthritis, injury, a combination thereof, etc.

Computer programs for creating the 3D computer generated bone models 22from the segmented 2D images 16 (i.e., bone contour lines) include:Analyze from AnalyzeDirect, Inc., Overland Park, Kans.; Insight Toolkit,an open-source software available from the National Library of MedicineInsight Segmentation and Registration Toolkit (“ITK”), www.itk.org; 3DSlicer, an open-source software available from www.slicer.org; Mimicsfrom Materialise, Ann Arbor, Mich.; and Paraview available atwww.paraview.org. Further, some embodiments may use customized softwaresuch as OMSegmentation (renamed “PerForm” in later versions), developedby OtisMed, Inc. The OMSegmentation software may extensively use “ITK”and/or “VTK” (Visualization Toolkit from Kitware, Inc, available atwww.vtk.org.) Some embodiments may include using a prototype ofOMSegmentation, and as such may utilize InsightSNAP software.

As indicated in FIG. 1C, the 3D computer generated bone models 22 areutilized to create 3D computer generated “restored bone models” or“planning bone models” 28 wherein the degenerated surfaces 24, 26 aremodified or restored to approximately their respective conditions priorto degeneration [block 115]. Thus, the bones 18, 20 of the restored bonemodels 28 are reflected in approximately their condition prior todegeneration. The restored bone models 28 are located such that point Pis at coordinates (X_(P), Y_(P), Z_(P)) relative to the origin (X₀, Y₀,Z₀). Thus, the restored bone models 28 share the same orientation andpositioning relative to the origin (X₀, Y₀, Z₀) as the bone models 22.

In one embodiment, the restored bone models 28 are manually created fromthe bone models 22 by a person sitting in front of a computer 6 andvisually observing the bone models 22 and their degenerated surfaces 24,26 as 3D computer models on a computer screen 9. The person visuallyobserves the degenerated surfaces 24, 26 to determine how and to whatextent the degenerated surfaces 24, 26 surfaces on the 3D computer bonemodels 22 need to be modified to restore them to their pre-degeneratedcondition. By interacting with the computer controls 11, the person thenmanually manipulates the 3D degenerated surfaces 24, 26 via the 3Dmodeling computer program to restore the surfaces 24, 26 to a state theperson believes to represent the pre-degenerated condition. The resultof this manual restoration process is the computer generated 3D restoredbone models 28, wherein the surfaces 24′, 26′ are indicated in anon-degenerated state.

In one embodiment, the bone restoration process is generally orcompletely automated. In other words, a computer program may analyze thebone models 22 and their degenerated surfaces 24, 26 to determine howand to what extent the degenerated surfaces 24, 26 surfaces on the 3Dcomputer bone models 22 need to be modified to restore them to theirpre-degenerated condition. The computer program then manipulates the 3Ddegenerated surfaces 24, 26 to restore the surfaces 24, 26 to a stateintended to represent the pre-degenerated condition. The result of thisautomated restoration process is the computer generated 3D restored bonemodels 28, wherein the surfaces 24′, 26′ are indicated in anon-degenerated state. For more detail regarding a generally orcompletely automated system for the bone restoration process, see U.S.patent application Ser. No. 12/111,924 to Park, which is titled“Generation of a Computerized Bone Model Representative of aPre-Degenerated State and Usable in the Design and Manufacture ofArthroplasty Devices”, was filed Apr. 29, 2008, and is incorporated byreference in its entirety into this Detailed Description.

As depicted in FIG. 1C, the restored bone models 28 are employed in apre-operative planning (“POP”) procedure to determine saw cut locations30 and drill hole locations 32 in the patient's bones that will allowthe arthroplasty joint implants to generally restore the patient's jointline to it pre-degenerative alignment [block 120].

In one embodiment, the POP procedure is a manual process, whereincomputer generated 3D implant models 34 (e.g., femur and tibia implantsin the context of the joint being a knee) and restored bone models 28are manually manipulated relative to each other by a person sitting infront of a computer 6 and visually observing the implant models 34 andrestored bone models 28 on the computer screen 9 and manipulating themodels 28, 34 via the computer controls 11. By superimposing the implantmodels 34 over the restored bone models 28, or vice versa, the jointsurfaces of the implant models 34 can be aligned or caused to correspondwith the joint surfaces of the restored bone models 28. By causing thejoint surfaces of the models 28, 34 to so align, the implant models 34are positioned relative to the restored bone models 28 such that the sawcut locations 30 and drill hole locations 32 can be determined relativeto the restored bone models 28.

In one embodiment, the POP process is generally or completely automated.For example, a computer program may manipulate computer generated 3Dimplant models 34 (e.g., femur and tibia implants in the context of thejoint being a knee) and restored bone models or planning bone models 28relative to each other to determine the saw cut and drill hole locations30, 32 relative to the restored bone models 28. The implant models 34may be superimposed over the restored bone models 28, or vice versa. Inone embodiment, the implant models 34 are located at point P′ (X_(P′),Y_(P′), Z_(P′)) relative to the origin (X₀, Y₀, Z₀), and the restoredbone models 28 are located at point P (X_(P), Y_(P), Z_(P)). To causethe joint surfaces of the models 28, 34 to correspond, the computerprogram may move the restored bone models 28 from point P (X_(P), Y_(P),Z_(P)) to point P′ (X_(P′), Y_(P′), Z_(P′)), or vice versa. Once thejoint surfaces of the models 28, 34 are in close proximity, the jointsurfaces of the implant models 34 may be shape-matched to align orcorrespond with the joint surfaces of the restored bone models 28. Bycausing the joint surfaces of the models 28, 34 to so align, the implantmodels 34 are positioned relative to the restored bone models 28 suchthat the saw cut locations 30 and drill hole locations 32 can bedetermined relative to the restored bone models 28. For more detailregarding a generally or completely automated system for the POPprocess, see U.S. patent application Ser. No. 12/563,809 to Park, whichis titled Arthroplasty System and Related Methods, was filed Sep. 21,2009, and is incorporated by reference in its entirety into thisDetailed Description.

While the preceding discussion regarding the POP process is given in thecontext of the POP process employing the restored bone models ascomputer generated 3D bone models, in other embodiments, the POP processmay take place without having to employ the 3D restored bone models, butinstead taking placing as a series of 2D operations. For more detailregarding a generally or completely automated system for the POP processwherein the POP process does not employ the 3D restored bone models, butinstead utilizes a series of 2D operations, see U.S. patent applicationSer. No. 12/546,545 to Park, which is titled Arthroplasty System andRelated Methods, was filed Aug. 24, 2009, and is incorporated byreference in its entirety into this Detailed Description.

As indicated in FIG. 1E, in one embodiment, the data 44 regarding thesaw cut and drill hole locations 30, 32 relative to point P′ (X_(P′),Y_(P′), Z_(P′)) is packaged or consolidated as the “saw cut and drillhole data” 44 [block 145]. The “saw cut and drill hole data” 44 is thenused as discussed below with respect to [block 150] in FIG. 1E.

As can be understood from FIG. 1D, the 2D images 16 employed to generatethe bone models 22 discussed above with respect to [block 110] of FIG.1C are also segmented along bone and cartilage boundaries to form boneand cartilage contour lines that are used to create computer generated3D bone and cartilage models (i.e., “arthritic models”) 36 of the bones18, 20 forming the patient's joint 14 [block 130]. Like theabove-discussed bone models 22, the arthritic models 36 are located suchthat point P is at coordinates (X_(P), Y_(P), Z_(P)) relative to theorigin (X₀, Y₀, Z₀) of the X-Y-Z axis [block 130]. Thus, the bone andarthritic models 22, 36 share the same location and orientation relativeto the origin (X₀, Y₀, Z₀). This position/orientation relationship isgenerally maintained throughout the process discussed with respect toFIGS. 1B-1E. Accordingly, movements relative to the origin (X₀, Y₀, Z₀)of the bone models 22 and the various descendants thereof (i.e., therestored bone models 28, bone cut locations 30 and drill hole locations32) are also applied to the arthritic models 36 and the variousdescendants thereof (i.e., the jig models 38). Maintaining theposition/orientation relationship between the bone models 22 andarthritic models 36 and their respective descendants allows the “saw cutand drill hole data” 44 to be integrated into the “jig data” 46 to formthe “integrated jig data” 48 employed by the CNC machine 10 tomanufacture the customized arthroplasty jigs 2.

Computer programs for creating the 3D computer generated arthriticmodels 36 from the segmented 2D images 16 (i.e., bone and cartilagecontour lines) include: Analyze from AnalyzeDirect, Inc., Overland Park,Kans.; Insight Toolkit, an open-source software available from theNational Library of Medicine Insight Segmentation and RegistrationToolkit (“ITK”), www.itk.org; 3D Slicer, an open-source softwareavailable from www.slicer.org; Mimics from Materialise, Ann Arbor,Mich.; and Paraview available at www.paraview.org. Some embodiments mayuse customized software such as OMSegmentation (renamed “PerForm” inlater versions), developed by OtisMed, Inc. The OMSegmentation softwaremay extensively use “ITK” and/or “VTK” (Visualization Toolkit fromKitware, Inc, available at www.vtk.org.). Also, some embodiments mayinclude using a prototype of OMSegmentation, and as such may utilizeInsightSNAP software.

Similar to the bone models 22, the arthritic models 36 depict the bones18, 20 in the present deteriorated condition with their respectivedegenerated joint surfaces 24, 26, which may be a result ofosteoarthritis, injury, a combination thereof, etc. However, unlike thebone models 22, the arthritic models 36 are not bone-only models, butinclude cartilage in addition to bone. Accordingly, the arthritic models36 depict the arthroplasty target areas 42 generally as they will existwhen the customized arthroplasty jigs 2 matingly receive thearthroplasty target areas 42 during the arthroplasty surgical procedure.

As indicated in FIG. 1D and already mentioned above, to coordinate thepositions/orientations of the bone and arthritic models 36, 36 and theirrespective descendants, any movement of the restored bone models 28 frompoint P to point P′ is tracked to cause a generally identicaldisplacement for the “arthritic models” 36 [block 135].

As depicted in FIG. 1D, computer generated 3D surface models 40 of thearthroplasty target areas 42 of the arthritic models 36 are importedinto computer generated 3D arthroplasty jig models 38 [block 140]. Thus,the jig models 38 are configured or indexed to matingly receive thearthroplasty target areas 42 of the arthritic models 36. Jigs 2manufactured to match such jig models 38 will then matingly receive thearthroplasty target areas of the actual joint bones during thearthroplasty surgical procedure.

In one embodiment, the procedure for indexing the jig models 38 to thearthroplasty target areas 42 is a manual process. The 3D computergenerated models 36, 38 are manually manipulated relative to each otherby a person sitting in front of a computer 6 and visually observing thejig models 38 and arthritic models 36 on the computer screen 9 andmanipulating the models 36, 38 by interacting with the computer controls11. In one embodiment, by superimposing the jig models 38 (e.g., femurand tibia arthroplasty jigs in the context of the joint being a knee)over the arthroplasty target areas 42 of the arthritic models 36, orvice versa, the surface models 40 of the arthroplasty target areas 42can be imported into the jig models 38, resulting in jig models 38indexed to matingly receive the arthroplasty target areas 42 of thearthritic models 36. Point P′ (X_(P′), Y_(P′), Z_(P′)) can also beimported into the jig models 38, resulting in jig models 38 positionedand oriented relative to point P′ (X_(P′), Y_(P′), Z_(P′)) to allowtheir integration with the bone cut and drill hole data 44 of [block125].

In one embodiment, the procedure for indexing the jig models 38 to thearthroplasty target areas 42 is generally or completely automated, asdisclosed in U.S. patent application Ser. No. 11/959,344 to Park, whichis entitled System and Method for Manufacturing Arthroplasty Jigs, wasfiled Dec. 18, 2007 and is incorporated by reference in its entiretyinto this Detailed Description. For example, a computer program maycreate 3D computer generated surface models 40 of the arthroplastytarget areas 42 of the arthritic models 36. The computer program maythen import the surface models 40 and point P′ (X_(P′), Y_(P′), Z_(P′))into the jig models 38, resulting in the jig models 38 being indexed tomatingly receive the arthroplasty target areas 42 of the arthriticmodels 36. The resulting jig models 38 are also positioned and orientedrelative to point P′ (X_(P′), Y_(P′), Z_(P′)) to allow their integrationwith the bone cut and drill hole data 44 of [block 125].

In one embodiment, the arthritic models 36 may be 3D volumetric modelsas generated from a closed-loop process. In other embodiments, thearthritic models 36 may be 3D surface models as generated from anopen-loop process.

As indicated in FIG. 1E, in one embodiment, the data regarding the jigmodels 38 and surface models 40 relative to point P′ (X_(P′), Y_(P′),Z_(P′)) is packaged or consolidated as the “jig data” 46 [block 145].The “jig data” 46 is then used as discussed below with respect to [block150] in FIG. 1E.

As can be understood from FIG. 1E, the “saw cut and drill hole data” 44is integrated with the “jig data” 46 to result in the “integrated jigdata” 48 [block 150]. As explained above, since the “saw cut and drillhole data” 44, “jig data” 46 and their various ancestors (e.g., models22, 28, 36, 38) are matched to each other for position and orientationrelative to point P and P′, the “saw cut and drill hole data” 44 isproperly positioned and oriented relative to the “jig data” 46 forproper integration into the “jig data” 46. The resulting “integrated jigdata” 48, when provided to the CNC machine 10, results in jigs 2: (1)configured to matingly receive the arthroplasty target areas of thepatient's bones; and (2) having cut slots and drill holes thatfacilitate preparing the arthroplasty target areas in a manner thatallows the arthroplasty joint implants to generally restore thepatient's joint line to its pre-degenerated state.

As can be understood from FIGS. 1A and 1E, the “integrated jig data” 44is transferred from the computer 6 to the CNC machine 10 [block 155].Jig blanks 50 are provided to the CNC machine 10 [block 160], and theCNC machine 10 employs the “integrated jig data” to machine thearthroplasty jigs 2 from the jig blanks 50.

For a discussion of example customized arthroplasty cutting jigs 2capable of being manufactured via the above-discussed process, referenceis made to FIGS. 1F-1I. While, as pointed out above, the above-discussedprocess may be employed to manufacture jigs 2 configured forarthroplasty procedures involving knees, elbows, ankles, wrists, hips,shoulders, vertebra interfaces, etc., the jig examples depicted in FIGS.1F-1I are for total knee replacement (“TKR”) or partial knee replacement(“PKR”) procedures. Thus, FIGS. 1F and 1G are, respectively, bottom andtop perspective views of an example customized arthroplasty femur jig2A, and FIGS. 1H and 1I are, respectively, bottom and top perspectiveviews of an example customized arthroplasty tibia jig 2B.

As indicated in FIGS. 1F and 1G, a femur arthroplasty jig 2A may includean interior side or portion 100 and an exterior side or portion 102.When the femur cutting jig 2A is used in a TKR or PKR procedure, theinterior side or portion 100 faces and matingly receives thearthroplasty target area 42 of the femur lower end, and the exteriorside or portion 102 is on the opposite side of the femur cutting jig 2Afrom the interior portion 100.

The interior portion 100 of the femur jig 2A is configured to match thesurface features of the damaged lower end (i.e., the arthroplasty targetarea 42) of the patient's femur 18. Thus, when the target area 42 isreceived in the interior portion 100 of the femur jig 2A during the TKRor PKR surgery, the surfaces of the target area 42 and the interiorportion 100 match.

The surface of the interior portion 100 of the femur cutting jig 2A ismachined or otherwise formed into a selected femur jig blank 50A and isbased or defined off of a 3D surface model 40 of a target area 42 of thedamaged lower end or target area 42 of the patient's femur 18.

As indicated in FIGS. 1H and 1I, a tibia arthroplasty jig 2B may includean interior side or portion 104 and an exterior side or portion 106.When the tibia cutting jig 2B is used in a TKR or PKR procedure, theinterior side or portion 104 faces and matingly receives thearthroplasty target area 42 of the tibia upper end, and the exteriorside or portion 106 is on the opposite side of the tibia cutting jig 2Bfrom the interior portion 104.

The interior portion 104 of the tibia jig 2B is configured to match thesurface features of the damaged upper end (i.e., the arthroplasty targetarea 42) of the patient's tibia 20. Thus, when the target area 42 isreceived in the interior portion 104 of the tibia jig 2B during the TKRor PKR surgery, the surfaces of the target area 42 and the interiorportion 104 match.

The surface of the interior portion 104 of the tibia cutting jig 2B ismachined or otherwise formed into a selected tibia jig blank 50B and isbased or defined off of a 3D surface model 40 of a target area 42 of thedamaged upper end or target area 42 of the patient's tibia 20.

II. Overview of Segmentation Process

A. Automatic Segmentation of Scanner Modality Image Data to Generate 3DSurface Model of a Patient's Bone

In one embodiment as mentioned above, the 2D images 16 of the patient'sjoint 14 as generated via the imaging system 8 (see FIG. 1A and [block100] of FIG. 1B) are segmented or, in other words, analyzed to identifythe contour lines of the bones and/or cartilage surfaces that are ofsignificance with respect to generating 3D models 22, 36, as discussedabove with respect to [blocks 110 and 130] of FIGS. 1C and 1D.Specifically, a variety of image segmentation processes may occur withrespect to the 2D images 16 and the data associated with such 2D images16 to identify contour lines that are then compiled into 3D bone models,such as bone models 22 and arthritic models 36. A variety of processesand methods for performing image segmentation are disclosed in theremainder of this Detailed Description.

The imager 8 typically generates a plurality of image slices 16 viarepetitive imaging operations. Depending on whether the imager 8 is aMRI or CT imager, each image slice will be a MRI or CT slice. As shownin FIG. 2A, the image slice may depict the cancellous bone 200, thecortical bone 202 surrounding the cancellous bone, and the articularcartilage lining portions of the cortical bone 202 of an object ofinterest of a joint, e.g., a femur 204 in a patient's knee joint 14. Theimage may further depict the cancellous bone 206, the cortical bone 208of another object of interest in the joint, e.g., a tibia 210 of theknee joint 14. In one embodiment, each image slice 16 may be atwo-millimeter 2D image slice.

One embodiment may automatically segment one or more features ofinterest (e.g., bones) present in MRI or CT scans of a patient joint,e.g., knee, hip, elbow, etc. A typical scan of a knee joint mayrepresent approximately a 100-millimeter by 150-millimeter by150-millimeter volume of the joint and may include about 40 to 80 slicestaken in sagittal planes. A sagittal plane is an imaginary plane thattravels from the top to the bottom of the object (e.g., the human body),dividing it into medial and lateral portions. It is to be appreciatedthat a large inter-slice spacing may result in voxels (volume elements)with aspect ratios of about one to seven between the resolution in thesagittal plane (e.g., the y z plane) and the resolution along the x axis(i.e., each scan slice lies in the yz plane with a fixed value of x).For example, a two-millimeter slice that is 150-millimeters by150-millimeters may be comprised of voxels that are approximately0.3-millimeter by 0.3-millimeter by 2-millimeters (for a 512 by 512image resolution in the sagittal plane).

In one embodiment, each slice may be a gray scale image with aresolution of 512 by 512 voxels where the voxel value represents thebrightness (intensity) of the voxel. The intensity may be stored as a16-bit integer resulting in an intensity range from 0 to 65,535, where 0may represent black and 65,535 may represent white. The intensity ofeach voxel typically represents the average intensity of the voxelvolume. Other embodiments may employ scans having higher or lowerresolutions in the sagittal plane, different inter-slice spacing, orimages where the intensity may be represented by a 24 bit vector (e.g.,eight bits each for a red component, green component and bluecomponent). Additionally, other embodiments may store intensity valuesas 32-bit signed integers or floating point values.

Typical MRI and CT scan data generally provide images where parts of abone boundary of interest may be well defined while other parts of thebone boundary may be difficult to determine due to voxel volumeaveraging, the presence of osteophyte growth, the presence of tissuehaving similar image intensities in neighboring areas to the object tobe segmented, amongst other things. Such poor definition of parts of thebone boundary in the images may cause traditional automated segmentationtechniques to fail. For example, FIG. 2A depicts regions 212 within aslice where an object boundary may not be visible due to neighboringtissue having about the same intensity as the feature of interest.Depicted in FIG. 2B are regions 214 that may be extended into the slicefrom adjacent slices due to a high voxel aspect ratio. Depicted in FIG.2C is a region 216 of the bone boundary 218 that may disappear or loseregularity when the bone boundary 218 is approximately tangent to theslice.

One embodiment may employ image segmentation techniques using a goldentemplate to segment bone boundaries and provide improved segmentationresults over traditional automated segmentation techniques. Suchtechniques may be used to segment an image when similarity betweenpixels within an object to be identified may not exist. That is, thepixels within a region to be segmented may not be similar with respectto some characteristic or computed property such as a color, intensityor texture that may be employed to associate similar pixels intoregions. Instead, a spatial relationship of the object with respect toother objects may be used to identify the object of interest. In oneembodiment, a 3D golden template of a feature of interest to besegmented may be used during the segmentation process to locate thetarget feature in a target scan. For example, when segmenting a scan ofa knee joint, a typical 3D image of a known good femur (referred to as agolden femur template) may be used to locate and outline (i.e., segment)a femur in a target scan.

Generally, much of the tissues surrounding the cancellous and corticalmatter of the bone to be segmented may vary from one MRI scan to anotherMRI scan. This may be due to disease and/or patient joint position(e.g., a patient may not be able to straighten the joint of interestbecause of pain). By using surrounding regions that have a stableconnection with the bone (e.g., the grown golden and boundary goldenregions of the template as described in more detail below), theregistration may be improved. Additionally, use of these regions allowsthe bone geometry of interest to be captured during the segmentationrather than other features not of interest. Further, the segmentationtakes advantage of the higher resolution of features of interest incertain directions of the scan data through the use of a combination of2D and 3D techniques, that selectively increases the precision of thesegmentation as described in more detail below with respect to refiningthe bone registration using an artificially generated image.

The segmentation method employed by one embodiment may accommodate avariety of intensity gradients across the scan data. FIGS. 3A-C depictintensity gradients (i.e., the intensity varies non-uniformly across theimage) in slices (an intensity gradient that is darker on the top andbottom as depicted in FIG. 3A, an intensity gradient that is darker onthe bottom as depicted in FIG. 3B, and an intensity gradient 220 that isbrighter on the sides as depicted in FIG. 3C) that may be segmented byone embodiment. Further, the embodiment generally does not requireapproximately constant noise in the slices to be segmented. Theembodiment may accommodate different noise levels, e.g., high noiselevels as depicted in FIG. 4A as well as low noise levels as depicted inFIG. 4B. The decreased sensitivity to intensity gradients and noiselevel typically is due to image registration techniques using a goldentemplate, allowing features of interest to be identified even though thefeature may include voxels with differing intensities and noise levels.

Segmentation generally refers to the process of partitioning a digitalimage into multiple regions (e.g., sets of pixels for a 2D image or setsof voxels in a 3D image). Segmentation may be used to locate features ofinterest (bones, cartilage, ligaments, etc.) and boundaries (lines,curves, etc. that represent the bone boundary or surface) in an image.In one embodiment, the output of the automatic segmentation of the scandata may be a set of images (scan slices 16) where each image 16includes a set of extracted closed contours representing bone outlinesthat identify respective bone location and shape for bones of interest(e.g., the shape and location of the tibia and femur in the scan data ofa knee joint). The generation of a 3D model correspondent to the aboveclosed contours may be additionally included into the segmentationprocess. The automatic or semi-automatic segmentation of a joint, usingimage slices 16 to create 3D models (e.g., bone models 22 and arthriticmodels 36) of the surface of the bones in the joint, may reduce the timerequired to manufacture customized arthroplasty cutting jigs 2. It is tobe appreciated that certain embodiments may generate open contours ofthe bone shapes of interest to further reduce time associated with theprocess.

In one embodiment, scan protocols may be chosen to provide gooddefinition in areas where precise geometry reconstruction is requiredand to provide lower definition in areas that are not as important forgeometry reconstruction. The automatic or semi-automatic imagesegmentation of one embodiment employs components whose parameters maybe tuned for the characteristics of the image modality used as input tothe automatic segmentation and for the features of the anatomicalstructure to be segmented, as described in more detail below.

In one embodiment, a General Electric 3T MRI scanner may be used toobtain the scan data. The scanner settings may be set as follows: pulsesequence: FRFSE-XL Sagittal PD; 3 Pane Locator—Scout Scan Thickness:4-millimeters; Imaging Options: TRF, Fast, FR; Gradient Mode: Whole; TE:approximately 31; TR: approximately 2100; Echo Train Length: 8;Bandwidth: 50 Hz; FOV: 16 centimeters, centered at the joint line; PhaseFOV: 0.8 or 0.9; Slice Thickness: 2 millimeters; Spacing: Interleave;Matrix: 384×192; NEX: 2; Frequency: SI; and Phase Correct: On. It is tobe appreciated that other scanners and settings may be used to generatethe scan data.

Typically, the voxel aspect ratio of the scan data is a function of howmany scan slices may be obtained while a patient remains immobile. Inone embodiment, a two-millimeter inter-slice spacing may be used duringa scan of a patient's knee joint. This inter-slice spacing providessufficient resolution for constructing 3D bone models of the patient'sknee joint, while allowing sufficiently rapid completion of scan beforethe patient moves.

FIG. 5 depicts a MRI scan slice that illustrates image regions wheregood definition may be needed during automatic segmentation of theimage. Typically, this may be areas where the bones come in contactduring knee motion, in the anterior shaft area next to the joint andareas located at about a 10- to 30-millimeter distance from the joint.Good definition may be needed in regions 230 of the tibia 232 andregions 234 of the femur 236. Regions 238 depict areas where the tibiais almost tangent to the slice and boundary information may be lost dueto voxel volume averaging.

Voxel volume averaging may occur during the data acquisition processwhen the voxel size is larger than a feature detail to be distinguished.For example, the detail may have a black intensity while the surroundingregion may have a white intensity. When the average of the contiguousdata enclosed in the voxel is taken, the average voxel intensity valuemay be gray. Thus, it may not be possible to determine in what part ofthe voxel the detail belongs.

Regions 240 depict areas where the interface between the cortical boneand cartilage is not clear (because the intensities are similar), orwhere the bone is damaged and may need to be restored, or regions wherethe interface between the cancellous bone and surrounding region may beunclear due to the presence of a disease formation (e.g., an osteophytegrowth which has an image intensity similar to the adjacent region).

FIG. 6 depicts a flowchart illustrating one method for automatic orsemi-automatic segmentation of Femur and Tibia Planning models of animage modality scan (e.g., an MRI scan) of a patient's knee joint.Initially, operation 250 obtains a scan of the patient's knee joint. Inone embodiment, the scan may include about 50 sagittal slices. Otherembodiments may use more or fewer slices. Each slice may be a gray scaleimage having a resolution of 512 by 512 voxels. The scan may representapproximately a 100-millimeter by 150-millimeter by 150-millimetervolume of the patient's knee. While the invention will be described foran MRI scan of a knee joint, this is by way of illustration and notlimitation. The invention may be used to segment other types of imagemodality scans such as computed tomography (CT) scans, ultrasound scans,positron emission tomography (PET) scans, etc., as well as other jointsincluding, but not limited to, hip joints, elbow joints, etc. Further,the resolution of each slice may be higher or lower and the images maybe in color rather than gray scale. It is to be appreciated thattransversal or coronal slices may be used in other embodiments.

After operation 250 obtains scan data (e.g., scan images 16) generatedby imager 8, operation 252 may be performed to segment the femur data ofthe scan data. During this operation, the femur may be located andspline curves 270 may be generated to outline the femur shape or contourlines in the scan slices, as depicted in FIGS. 7A-7K. It should beappreciated that one or more spline curves may be generated in eachslice to outline the femur contour depending on the shape and curvatureof the femur as well as the femur orientation relative to the slicedirection.

Next, in operation 254, a trained technician may verify that thecontours of the femur spline curves generated during operation 252follow the surface of the femur bone. The technician may determine thata spline curve does not follow the bone shape in a particular slice. Forexample, FIG. 8 depicts an automatically generated femur spline curve274. The technician may determine that the curve should be enlarged inthe lower left part 276 of the femur. There may be various reasons whythe technician may decide that the curve needs to be modified. Forexample, a technician may want to generate a pre-deteriorated boneshape, yet the bone may be worn out in this region and may needreconstruction. The technician may determine this by examining theoverall 3D shape of the segmented femur and also by comparing lateraland medial parts of the scan data. The segmented region of the slice maybe enlarged by dragging one or more control points 278 located on thespline curve 274 to adjust the curve to more closely follow the femurboundary as determined by the technician, as shown by adjusted curve280. The number of control points on a spline curve may be dependent onthe curve length and curvature variations. Typically, 10-25 controlpoints may be associated with a spline curve for spline modification.

Once the technician is satisfied with all of the femur spline curves inthe scan slices, operation 256 generates a watertight triangular meshgeometry from the femur segmentation that approximates the 3D surface ofthe femur. The mesh closely follows the femur spline curves 270 andsmoothly interpolates between them to generate a 3D surface model of thefemur. FIG. 9 depicts typical 3D mesh geometry 290 of a target femurgenerated by one embodiment. Such a 3D model may be a 3D surface modelor 3D volume model resulting from open-loop contour lines or closed loopcontour lines, respectively. In one embodiment, such a 3D model asdepicted in FIG. 9 may be a bone model 22 or an arthritic model 36.

After operation 256, operation 258 may be performed to segment the tibiadata in the scan data. During this operation, the tibia is located andspline curves may be generated to locate and outline the shape of thetibia found in the scan slices, as depicted by tibia spline curves 272in FIGS. 7A-7K. It should be appreciated that one or more spline curvesmay be generated in each slice to outline the tibia depending on theshape and curvature of the tibia as well as the tibia orientationrelative to the slice direction.

Next, in operation 260, the technician may verify the tibia splinecurves generated during operation 258. The technician may determine thata spline curve does not follow the tibia in a particular slice. Forexample, referring back to FIG. 8, an automatically generated tibiaspline curve 282 is depicted that may not follow the tibia in the rightpart of the tibia due to the presence of an osteophyte growth 284. Thepresence of the osteophyte growth 284 may be determined by examiningneighboring slices. In this case, the segmented region may be reduced bydragging one or more control points 286 located on the spline curve tomodify the tibia spline curve 282 to obtain the adjusted tibia splinecurve 288. As previously discussed, each spline curve may haveapproximately 10-25 control points depending on the length and curvaturevariation of the spline curve.

When the purpose of the segmentation is generating bone models that willbe shown to a surgeon in the images where they are overlapped byimplants, a technician will not need to restore the segmentation modelto its pre-deteriorated bone shape, and thus will not need to spend timeon adjusting splines to follow the pre-deteriorated bone shape. Alsothere is no need to get highly precise segmentation in the bone areasthat are to be replaced with implant. So there is no need to spend timeon adjusting the non-perfect curves in the “to be replaced” areas.

Once the technician is satisfied with all of the tibia spline curves inthe scan slices, operation 262 generates a watertight triangular meshgeometry from the tibia segmentation. The mesh closely follows thespline curves and smoothly interpolates between them to generate a 3Dsurface model of the tibia. FIG. 10 depicts a typical 3D mesh geometry292 of a target tibia generated by one embodiment. Such a 3D model maybe a 3D surface model or 3D volume model resulting from open-loopcontour lines or closed loop contour lines, respectively. In oneembodiment, such a 3D model as depicted in FIG. 10 may be a bone model22 or an arthritic model 36.

Because the objects to be located in the scan data typically cannot besegmented by grouping similar voxels into regions, a golden templaterepresentative of a typical size and shape of the feature of interestmay be employed during the segmentation process to locate the targetfeature of interest.

FIG. 11 depicts a flowchart illustrating one method for generating agolden template. The method will be described for generating a goldentemplate of a tibia by way of illustration and not limitation. Themethod may be used to generate golden templates of other bonesincluding, but not limited to a femur bone, a hip bone, etc.

Initially, operation 300 obtains a scan of a tibia that is not damagedor diseased. The appropriate tibia scan may be chosen by screeningmultiple MRI tibia scans to locate a MRI tibia scan having a tibia thatdoes not have damaged cancellous and cortical matter (i.e., no damage intibia regions that will be used as fixed images to locate acorresponding target tibia in a target scan during segmentation), whichhas good MRI image quality, and which has a relatively average shape,e.g., the shaft width relative to the largest part is not out ofproportion (which may be estimated by eye-balling the images). Thistibia scan data, referred to herein as a golden tibia scan, may be usedto create a golden tibia template. It is to be appreciated that severalMRI scans of a tibia (or other bone of interest) may be selected, atemplate generated for each scan, statistics gathered on the successrate when using each template to segment target MRI scans, and the onewith the highest success rate selected as the golden tibia template.

In other embodiments, a catalog of golden models may be generated forany given feature, with distinct variants of the feature depending onvarious patient attributes, such as (but not limited to) weight, height,race, gender, age, and diagnosed disease condition. The appropriategolden mesh would then be selected for each feature based on a givenpatient's characteristics.

Then, in operation 302 the tibia is segmented in each scan slice. Eachsegmentation region includes the cancellous matter 322 and corticalmatter 324 of the tibia, but excludes any cartilage matter to form agolden tibia region, outlined by a contour curve 320, as depicted inFIG. 12A.

Next, operation 304 generates a golden tibia mesh 340 from theaccumulated golden tibia contours of the image slices, as illustrated inFIG. 13A.

Next, operation 306 increases the segmented region in each slice bygrowing the region to include boundaries between the tibia and adjacentstructures where the contact area is generally relatively stable fromone MRI scan to another MRI scan. This grown region may be referred toherein as a grown golden tibia region, outlined by contour curve 328, asdepicted in FIG. 12A.

The grown golden region may be used to find the surface that separatesthe hard bone (cancellous and cortical) from the outside matter(cartilage, tendons, water, etc.). The changes in voxel intensities whengoing from inside the surface to outside of the surface may be used todefine the surface. The grown golden region may allow the registrationprocess to find intensity changes in the target scan that are similar tothe golden template intensity changes near the surface. Unfortunately,the golden segmentation region does not have stable intensity changes(e.g., near the articular surface) or may not have much of an intensitychange. Thus, the grown region typically does not include such regionsbecause they do not provide additional information and may slow down theregistration due to an increased number of points to be registered.

Finally, use of a grown golden region may increase the distance wherethe metric function detects a feature during the registration process.When local optimization is used, the registration may be moved in aparticular direction only when a small movement in that directionimproves the metric function. When a golden template feature is fartheraway from the corresponding target bone feature (e.g., when there is asignificant shape difference), the metric typically will not move towardthat feature. Use of the larger grown region may allow the metric todetect the feature and move toward it.

Next, operation 308 cuts off most of the inner part of the grown goldentibia region to obtain a boundary golden tibia region 330 depicted inFIG. 12A. The boundary golden tibia region 330 is bounded on the insideby contour curve 332 and the outside by contour curve 328.

The boundary region may be used to obtain a more precise registration ofthe target bone by using the interface from the cancellous bone to thecortical bone. This may be done so that intensity variations in otherareas (e.g., intensity variations deep inside the bone) that may movethe registration toward wrong features and decrease the precision oflocating the hard bone surface are not used during the registration.

Then, operation 310 applies Gaussian smoothing with a standard deviationof two pixels to every slice of the golden tibia scan. In oneembodiment, a vtkImageGaussianSmooth filter (part of VisualizationToolkit, a free open source software package) may be used to perform theGaussian smoothing by setting the parameter “Standard Deviation” to avalue of two.

Then, operation 312 generates an anchor segmentation. The anchorsegmentation typically follows the original segmentation where the tibiaboundary is well defined in most MRI scans. In areas where the tibiaboundary may be poorly defined, but where there is another well-definedfeature close to the tibia boundary, the anchor segmentation may followthat feature instead. For example, in an area where a healthy bonenormally has cartilage, a damaged bone may or may not have cartilage. Ifcartilage is present in this damaged bone region, the bone boundaryseparates the dark cortical bone from the gray cartilage matter. Ifcartilage is not present in this area of the damaged bone, there may bewhite liquid matter next to the dark cortical bone or there may beanother dark cortical bone next to the damaged bone area. Thus, theinterface from the cortical bone to the outside matter in this region ofthe damaged bone typically varies from MRI scan to MRI scan. In suchareas, the interface between the cortical and the inner cancellous bonemay be used. These curves may be smoothly connected together in theremaining tibia areas to obtain the tibia anchor segmentation curve 358,depicted in FIG. 14A.

Then, operation 314 may determine three disjoint regions along theanchor segmentation boundary. Each of these regions is generally welldefined in most MRI scans. FIG. 14A depicts these three disjoint regionsfor a particular image slice. The first region 350, referred to hereinas the tibia InDark-OutLight region, depicts a region where the anchorsegmentation boundary separates the inside dark intensity corticalmatter voxels from the outside light intensity voxels. The second region352, referred to herein as the tibia InLight-OutDark region, depicts aregion where the boundary separates the inside light intensitycancellous matter voxels from the outside dark intensity cortical mattervoxels. Finally, region 354, referred to herein as the tibiaDark-in-Light region, depicts a region that has a very thin layer ofdark intensity cortical matter voxels along the boundary, but which haslight intensity matter voxels away from the boundary (i.e., on bothsides of the boundary). Generally, the other regions along the anchorsegmentation boundary vary from scan to scan or may not be clear in mostof the scans, as depicted by regions 356. Such regions may be anosteophyte growth with an arbitrary shape but which has about the sameintensity as the region next to it. Thus, such regions typically are notused as anchor regions in one embodiment of the invention.

Finally, operation 316 generates a mesh corresponding to the anchorsegmentation and also generates a mesh for each anchor region. FIG. 15Adepicts the anchor segmentation mesh 360, the InDark-OutLight anchorregion mesh 362, the InLight-OutDark anchor region mesh 364 and theDark-in-Light anchor region mesh 366 for the tibia. These 3D meshesmodel the surface of the golden tibia in the specified regions. It is tobe appreciated that the 3D meshes are distinct and generally are notcombined to create a composite mesh. These meshes may be used to createan artificial fixed image that is used during the registration processas described in more detail below.

A golden template of a femur may also be generated in a similar mannerusing the method depicted by FIG. 11. FIG. 12B depicts the golden femurregion, outlined by a contour curve 320A, the grown femur region,outlined by contour curve 328A, and the boundary golden femur region330A bounded on the inside by contour curve 332A and the outside bycontour curve 328A. FIG. 13B depicts the golden femur mesh 340A. FIG.14B depicts the femur anchor segmentation curve 358A, the femurInDark-OutLight region 350A and the femur InLight-OutDark region 352A.Finally, FIG. 15B depicts the anchor segmentation mesh 360A, theInDark-OutLight anchor region mesh 362A and the InLight-OutDark anchorregion mesh 364A for the femur.

FIG. 16 depicts a flowchart illustrating one method for performingautomatic segmentation (e.g., operation 252 or operation 258 of FIG. 6)of the scan data of a joint (e.g., a MRI scan of a knee joint) usinggolden template registration. The segmentation method may be used tosegment the femur (operation 252 of FIG. 6) and/or the tibia (operation258 of FIG. 6) in either the left or right knee. Different goldentemplate data may be used to segment the left tibia, right tibia, leftfemur or right femur. Additionally, other embodiments may segment otherjoints, including but not limited to, hip joints, elbow joints, by usingan appropriate golden template of the feature of interest to besegmented.

Initially, operation 370 maps the segmented 3D golden template andmarked regions (e.g., grown and boundary regions) to the target scandata using image registration techniques. This may be done to locate thecorresponding feature of interest in the target scan (e.g., a targetfemur or tibia). Registration transforms the template image coordinatesystem into the target coordinate system. This allows the template imageto be compared and/or integrated with the target image.

Next, operation 372 refines the registration near the feature (e.g., abone) boundary of interest. Anchor segmentation and anchor regions maybe used with a subset of 3D free-form deformations to move points withinthe plane of the slices (e.g., the yz plane) but not transversal (alongthe x axis) to the slices. Refinement of the initial registrationoperation may be necessary to correct errors caused by a high voxelaspect ratio. When a point from a golden template is mapped onto thetarget scan, it generally maps to a point between adjacent slices of thescan data. For example, if a translation occurs along the x direction,then the point being mapped may only align with a slice when thetranslation is a multiple of the inter-slice scan distance (e.g., amultiple of two-millimeters for an inter-slice spacing oftwo-millimeters). Otherwise, the point will be mapped to a point thatfalls between slices. In such cases, the intensity of the target scanpoint may be determined by averaging the intensities of correspondingpoints (voxels) in the two adjacent slices. This may further reduceimage resolution. Additionally, refinement of the initial registrationoperation may correct for errors due to unhealthy areas and/or limitedcontrast areas. That is, the golden template may be partially pulledaway from the actual bone boundary in diseased areas and/or minimalcontrast areas (e.g., toward a diseased area having a differentcontrast) during the initial registration operation.

Next, operation 374 generates a polygon mesh representation of thesegmented scan data. A polygon mesh typically is a collection ofvertices, edges, and faces that may define the surface of a 3D object.The faces may consist of triangles, quadrilaterals or other simpleconvex polygons. In one embodiment, a polygon mesh may be generated byapplying the registration transform found during operation 372 to allthe vertices of a triangle golden template mesh (i.e., the surface ofthe mesh is composed of triangular faces). It is to be appreciated thatthe cumulative registration transform typically represents the transformthat maps the golden template into the target MRI scan with minimalmisalignment error.

Finally, operation 376 generates spline curves that approximate theintersection of the mesh generated by operation 374 with the target MRIslices. Note that these spline curves may be verified by the technician(during operation 254 or operation 260 of FIG. 6).

FIG. 17 depicts a flowchart illustrating one method for mapping thesegmented golden femur template regions into the target scan using imageregistration techniques. Registration may be thought of as anoptimization problem with a goal of finding a spatial mapping thataligns a fixed image with a target image. Generally several registrationoperations may be performed, first starting with a coarse imageapproximation and a low-dimensional transformation group to find a roughapproximation of the actual femur location and shape. This may be doneto reduce the chance of finding wrong features instead of the femur ofinterest. For example, if a free-form deformation registration wasinitially used to register the golden femur template to the target scandata, the template might be registered to the wrong feature, e.g., to atibia rather than the femur of interest. A coarse registration may alsobe performed in less time than a fine registration, thereby reducing theoverall time required to perform the registration. Once the femur hasbeen approximately located using a coarse registration, finerregistration operations may be performed to more accurately determinethe femur location and shape. By using the femur approximationdetermined by the prior registration operation as the initialapproximation of the femur in the next registration operation, the nextregistration operation may find a solution in less time.

In one embodiment, each registration operation may employ a registrationframework 390 as depicted in FIG. 18. The registration framework 390 mayemploy an image similarity-based method. Such a method generallyincludes a transformation model T(X) 392, which may be applied tocoordinates of a fixed (or reference) image 394 (e.g., a golden femurtemplate) to locate their corresponding coordinates in a target image396 space (e.g., a MRI scan), an image similarity metric 398, whichquantifies the degree of correspondence between features in both imagespaces achieved by a given transformation, and an optimizer 400, whichtries to maximize image similarity (or minimize an opposite function) bychanging the parameters of the transformation model 392. An interpolator402 may be used to evaluate target image intensities at non-gridlocations (e.g., reference image points that are mapped to target imagepoints that lie between slices). Thus, a registration frameworktypically includes two input images, a transform, a metric, aninterpolator and an optimizer.

Referring again to FIG. 17, operation 380 may approximately register agrown femur region in a MRI scan using a coarse registrationtransformation. In one embodiment, this may be done by performing anexhaustive translation transform search on the MRI scan data to identifythe appropriate translation transform parameters that minimizestranslation misalignment of the reference image femur mapped onto thetarget femur of the target image. This coarse registration operationtypically determines an approximate femur position in the MRI scan.

A translational transform, translates (or shifts) all elements of animage by the same 3D vector. That is, the reference femur may be mappedinto the target image space by shifting the reference femur along one ormore axes in the target image space to minimize misalignment. Duringthis operation the reference femur is not rotated, scaled or deformed.In one embodiment, three parameters for the translation transformationmay be generated: one parameter for each dimension that specifies thetranslation for that dimension. The final parameters of the translationtransform minimizing the misalignment of the mapped reference femurimage coordinates into the target image space may be stored.

Next, operation 382 further refines the image registration determined byoperation 380. This may be done by approximately registering the grownfemur region of the reference golden template femur into the target MRIscan data using a similarity transformation. In one embodiment, asimilarity transformation may be performed in 3D space. The referencegolden femur region may be rotated in 3D, translated in 3D andhomogeneously scaled to map its coordinates into the target MRI scandata to minimize misalignment between the reference golden femur regionand the corresponding region in the target MRI scan. In someembodiments, a center of rotation may be specified so that both therotation and scaling operations are performed with respect to thespecified center of rotation. In one embodiment, a 3D similaritytransformation, specified by seven parameters, may be used. Oneparameter specifies the scaling factor, three parameters specify aversor that represents the 3D rotation and three parameters specify avector that represents the 3D translation in each dimension. A versor isa unit quaternion that provides a convenient mathematical notation forrepresenting orientations and rotations of objects in three dimensions.

In one embodiment, local minimization techniques may be employed withthe similarity transformation to obtain a refined registration of thereference golden femur region onto the target MRI scan that is not farfrom the registration of the reference golden femur region onto thetarget MRI scan found in the previous operation 190 and used as theinitial starting approximation. Registering the grown golden femurregion may increase the distance where the metric function detects afeature during the registration process. When local optimization isused, the registration may be moved in a particular direction only whena small movement in that direction improves the metric function. When agolden femur template feature is farther away from the correspondingtarget femur feature (e.g., when there is a significant shapedifference), the metric typically will not move toward that feature. Useof the larger grown femur region may allow the metric to detect thefeature and move toward it.

After operation 382, operation 384 further refines the imageregistration of the golden femur into the target scan. In oneembodiment, an affine transformation may be used to register coordinatesof a boundary golden femur region of a golden femur template into thetarget MRI scan data. In one embodiment, the approximate femurregistration found during operation 382 may be used as the initialstarting approximation for the affine transformation.

An affine transformation typically is a linear transformation followedby a translation. The affine transformation preserves collinearitybetween points (i.e., three points which lie on a line continue to becollinear after the transformation) and ratios of distances along aline. In one embodiment, a 3D affine transformation, specified by 12parameters, may be utilized. Nine parameters of the affinetransformation specify the linear transformation (which may berepresented by a three by three matrix) and three parameters of theaffine transformation specify the 3D translation in each dimension. Theparameters of the affine transform that minimizes the misalignment ofthe boundary golden femur region mapped into the target MRI scan datamay be stored.

Finally, operation 386 further refines the image registration of theboundary golden femur region. In one embodiment, a spline transformationmay be used to register the coordinates of the boundary golden femurregion into the MRI scan data (target image space). In one embodiment, a3D B-Spline deformable transformation may be employed and thetransformation found in operation 384 may be used as the initialtransformation values for the 3D B-Spline deformable transformation.

A B-Spline deformable transformation typically is a free formdeformation of an object using a deformation field where a deformationvector is assigned to every point in space. For example, a 3D B-splinedeformable transform T may specify a 3D vector V(P) for every point P inthe original 3D space that is moved by T such that T:P→P+V(P).

In one embodiment, a B-Spline transformation may be specified with M×Nparameters, where M is the number of nodes in the B-Spline grid and N isthe dimension of the space. In one embodiment, a 3D B-Spline deformabletransformation of order three may be used to map every reference image3D point into the target MRI scan by a different 3D vector. The field ofthe vectors may be modeled using B-splines. Typically a grid J×K×L ofcontrol points may be specified where J, K, and L are parameters of thetransformation.

In one embodiment, splines of order three may be used with a grid 9×6×6of control points. That is, the transformation employs nine controlpoints in the medial/lateral direction (i.e., the x direction), and sixcontrol points in the other directions (i.e., y and z directions). Twocontrol points in each dimension (i.e., 2 of 9 in the x direction, 2 of6 in the y direction and 2 of 6 in the z direction) may be used tospecify boundary conditions. As such, the inner spline nodes may form agrid of size 7 by 4 by 4 and the boundary conditions increase the gridto size 9 by 6 by 6. The parametric set for this transformation has adimension of 3×9×6×6=972 (i.e., each dimension may have a 9×6×6 grid ofcontrol points). The final parameters of the spline transformation thatminimizes the misalignment between the reference golden femur templateand the target MRI scan data may be stored. This may be referred to asthe cumulative femur registration transform herein.

FIG. 19 depicts a flowchart illustrating one method for mapping thesegmented golden tibia template regions into the target scan using imageregistration techniques. Generally several registration operations maybe performed, first starting with a coarse image approximation and alow-dimensional transformation group to find a rough approximation ofthe actual tibia location and shape. This may be done to reduce thechance of finding wrong features instead of the tibia of interest. Forexample, if a free-form deformation registration was initially used toregister the golden tibia template to the target scan data, the templatemight be registered to the wrong feature, e.g., to a femur rather thanthe tibia of interest. A coarse registration may also be performed inless time than a fine registration, thereby reducing the overall timerequired to perform the registration. Once the tibia has beenapproximately located using a coarse registration, finer registrationoperations may be performed to more accurately determine the tibialocation and shape. By using the tibia approximation determined by theprior registration operation as the initial approximation of the tibiain the next registration operation, the next registration operation mayfind a solution in less time.

In one embodiment, each registration operation may employ a registrationframework 390 as depicted in FIG. 18. The registration framework 390 mayemploy an image similarity-based method. Such a method generallyincludes a transformation model T(X) 392, which may be applied tocoordinates of a fixed (or reference) image 394 (e.g., a golden tibiatemplate) to locate their corresponding coordinates in a target image396 space (e.g., a MRI scan), an image similarity metric 398, whichquantifies the degree of correspondence between features in both imagespaces achieved by a given transformation, and an optimizer 400, whichtries to maximize image similarity by changing the parameters of thetransformation model 392. An interpolator 402 may be used to evaluatetarget image intensities at non-grid locations (i.e., reference imagepoints that are mapped to target image points that lie between slices).Thus, a registration framework typically includes two input images, atransform, a metric, an interpolator and an optimizer.

The automatic segmentation registration process will be described usingscan data that includes a right tibia bone. This is by way ofillustration and not limitation. Referring again to FIG. 19, operation410 may approximately register a grown tibia region in a MRI scan usinga coarse registration transformation. In one embodiment, this may bedone by performing an exhaustive translation transform search on the MRIscan data to identify the appropriate translation transform parametersthat minimizes translation misalignment of the reference image tibiamapped onto the target tibia of the target image. This coarseregistration operation typically determines an approximate tibiaposition in the MRI scan. During this operation, the tibia of thereference image may be overlapped with the target tibia of the targetimage using a translation transformation to minimize translationalmisalignment of the tibias.

A translational transform, translates (or shifts) an image by the same3D vector. That is, the reference tibia may be mapped into the targetimage space by shifting the reference tibia along one or more axes inthe target image space to minimize misalignment. During this operationthe reference tibia is not rotated, scaled or deformed. In oneembodiment, three parameters for the translation transformation may begenerated, one parameter for each dimension that specifies thetranslation for that dimension. The final parameters of the translationtransform minimizing the misalignment of the mapped reference tibiaimage coordinates into the target image space may be stored.

Next, operation 412 further refines the image registration determined byoperation 410. This may be done by approximately registering the growntibia region of the reference golden tibia template into the target MRIscan data using a similarity transformation. In one embodiment, asimilarity transformation may be performed in 3D space. The referencegolden tibia region may be rotated in 3D, translated in 3D andhomogeneously scaled to map its coordinates into the target MRI scandata to minimize misalignment between the reference golden tibia regionand the corresponding region in the target MRI scan. In someembodiments, a center of rotation may be specified so that both therotation and scaling operations are performed with respect to thespecified center of rotation. In one embodiment, a 3D similaritytransformation, specified by seven parameters, may be used. Oneparameter specifies the scaling factor, three parameters specify aversor that represents the 3D rotation and three parameters specify avector that represents the 3D translation in each dimension. A versor isa unit quaternion that provides a convenient mathematical notation forrepresenting orientations and rotations of objects in three dimensions.

In one embodiment, local minimization techniques may be employed withthe similarity transformation to obtain a refined registration of thereference golden tibia region onto the target MRI scan that is not farfrom the registration of the reference golden tibia region onto thetarget MRI scan found in the previous operation 410 and used as theinitial starting approximation. Registering the grown golden tibiaregion may increase the distance where the metric function detects afeature during the registration process. When local optimization isused, the registration may be moved in a particular direction only whena small movement in that direction improves the metric function. When agolden tibia template feature is farther away from the correspondingtarget tibia feature (e.g., when there is a significant shapedifference), the metric typically will not move toward that feature. Useof the larger grown tibia region may allow the metric to detect thefeature and move toward it.

After operation 412, operation 414 further refines the imageregistration. In one embodiment, an affine transformation may be used toregister coordinates of a boundary golden tibia region of a golden tibiatemplate into the target MRI scan data. In one embodiment, theapproximate tibia registration found during operation 412 may be used asthe initial starting approximation for the affine transformation.

An affine transformation typically is a linear transformation followedby a translation. The affine transformation preserves collinearitybetween points (i.e., three points which lie on a line continue to becollinear after the transformation) and ratios of distances along aline. In one embodiment, a 3D affine transformation, specified by 12parameters, may be utilized. Nine parameters of the affinetransformation specify the linear transformation (which may berepresented by a three by three matrix) and three parameters of theaffine transformation specify the 3D translation in each dimension. Theparameters of the affine transform that minimizes the misalignment ofthe boundary golden tibia region mapped into the target MRI scan datamay be stored.

Finally, operation 416 further refines the image registration of theboundary golden tibia region. In one embodiment, a spline transformationmay be used to register the coordinates of the boundary golden tibiaregion into the MRI scan data (target image space). In one embodiment, a3D B-Spline deformable transformation may be employed and thetransformation found in operation 414 may be used as the initialtransformation values for the 3D B-Spline deformable transformation.

A B-Spline deformable transformation typically is a free formdeformation of an object using a deformation field where a deformationvector is assigned to every point in space. In one embodiment, aB-Spline transformation may be specified with M×N parameters, where M isthe number of nodes in the B-Spline grid and N is the dimension of thespace. In one embodiment, a 3D B-Spline deformable transformation oforder three may be used to map every reference image 3D point into thetarget MRI scan by a different 3D vector. The field of the vectors maybe modeled using B-splines. Typically a grid J×K×L of control points maybe specified where J, K, and L are parameters of the transformation.

In one embodiment, splines of order three may be used with a grid 9×6×6of control points. That is, the transformation employs nine controlpoints in the medial/lateral direction (i.e., the x direction, and sixcontrol points in the other directions (i.e., the y and z directions).Two control points in each dimension (i.e., 2 of 9 in the x direction, 2of 6 in the y direction and 2 of 6 in the z direction) may be used tospecify boundary conditions. As such, the inner spline nodes may form agrid of size 7 by 4 by 4 and the boundary conditions increase the gridto size 9 by 6 by 6. The parametric set for this transformation has adimension of 3×9×6×6=972. The final parameters of the splinetransformation that minimizes the misalignment between the referencegolden tibia template and the target MRI scan data may be stored. Thismay be referred to as the cumulative tibia registration transformherein.

The shape of the tibia may vary more from patient to patient than doesthe shape of the femur. As a result, the affine transformation may notprovide a close enough registration of the golden tibia template to thetarget tibia in the target scan. This may cause the Splinetransformation to find a local optimum that may be far from the actualtibia in some areas. In one embodiment, an additional registrationoperation between the affine transform and spline transform operationsmay be performed to more closely align the golden tibia and the targettibia, allowing the spline transform to converge to the correct localoptimum rather than a nearby (but wrong) local optimum.

The class of transforms utilized generally should allow more flexibility(or degrees of freedom) than the Affine transform and less flexibilitythan the B-spline transforms. The number of degrees of freedom generallyis equal to the number of transform parameters. In one embodiment, aclass of transforms with more than 12 parameters and less than 3×9×6×6parameters may be used. For example, a B-spline transform with fewercontrol points (than used in the subsequent spline transform) may beused for the additional transform operation. Alternatively, thedeformations may be modeled using quadric rather than cubic functions.

In another embodiment, several golden tibia templates may be used thatrepresent typical tibia variations, e.g., golden tibia templates forvarum, valgum and normal tibia. In one embodiment, each of the goldentibia templates may be used during the translation, similarity andaffine transform registration operations to find the template thatprovides the best match (e.g., best correlation) in the affine transformregistration operation. This template may then be used in the remainingregistration operations.

Finally, in one embodiment, the tibia registration may be improved byperforming the tibia segmentation after the femur segmentation andadding a restriction on the tibia registration transformations such thatthe tibia may not penetrate the femur. In one embodiment, this may beimplemented by introducing a penalty for the penetration. In the targetMRI all the voxels that lie inside the femur splines may be marked. Themetric functions, described in more detail below, that are used in theregistration operations may be modified to include a penalty term. Thepenalty term may be computed by selecting a set of points on theboundary of the golden template segmentation, applying a transform tothe set of points (in a similar way as the transform is applied to thesample points used in the correlation computations), determining if atransformed sample point falls into any of the marked voxels, and addinga large value to the penalty term for each transformed sample point thatfalls into any of the marked voxels.

In each of the above registration operations, a metric may be used toquantify the degree of correspondence between features in both thereference image and target image achieved by a given transformation. Inone embodiment, the metric quantitatively measures how well thetransformed golden template image fits the target image (e.g., a targetMRI scan) and may compare the gray-scale intensity of the images using aset of sample points in the golden template region to be registered.

FIG. 20 depicts a flowchart illustrating one method for computing themetric used by the registration operations described above. For aparticular registration operation, the metric may be computed in thesame way, but the metric may have different parameters specified for theparticular registration operation. The metric may be referred to hereinas “local correlation in sample points.” Initially, operation 420selects a set of sample points in the golden template region to beregistered.

For the translation and similarity transformations, the sample pointsmay be selected as follows. Initially, a rectilinear grid of L×M×N thatcovers the whole bone in 3D space may be used. L, M, and N may vary fromone to 16. In one embodiment, an eight by eight grid in every imageslice may be used to select uniform sample points in the grown goldenregion of the golden template. For each grid cell, the first samplepoint is selected. If the sample point falls within the grown goldenregion, it is used. If the sample point falls outside the golden region,it is discarded.

For the affine and spline transformations, the sample points may bedetermined by randomly selecting one out of every 32 points in theboundary golden region of the MRI slice.

Next, operation 422 groups the selected points into buckets. In oneembodiment, buckets may be formed as follows. First, the 3D space may besubdivided into cells using a rectilinear grid. Sample points thatbelong to the same cell are placed in the same bucket. It should benoted that sample points may be grouped into buckets to compensate fornon-uniform intensities in the MRI scan.

For example, MRI scan data may be brighter in the middle of the imageand darker towards the edges of the image. This brightness gradienttypically is different for different scanners and may also depend onother parameters including elapsed time since the scanner was lastcalibrated. Additionally, high aspect ratio voxels typically result invoxel volume averaging. That is, cortical bone may appear very dark inareas where its surface is almost perpendicular to the slice andgenerally will not be averaged with nearby tissues. However, corticalbone may appear as light gray in the areas where its surface is almosttangent to the slice and generally may be averaged with a large amountof nearby tissues.

Next, operation 424 sub-samples the target MRI slice. Sub-sampling thetarget space generally has the effect of smoothing the metric function.This may remove tiny local minima such that the local minimizationalgorithm converges to a deeper minimum. In one embodiment, duringoperations 410 and 412 (of FIG. 19), each slice may be sub-sampled withan eight by eight grid. During operations 414 and 416 (of FIG. 19), eachslice may be sub-sampled with a four by four grid. That is, during thesub-sampling, one point from every grid cell may be selected (e.g., thefirst point) and the remaining points in the grid cells may bediscarded.

Next, operation 426 computes a correlation of the intensities of thepoints in each bucket and their corresponding points in the target MRIscan (after mapping). The correlation (NC) metric may be expressed as:

${{NC}\left( {A,B} \right)} = {\frac{\sum\limits_{i = 1}^{N}{A_{i}B_{i}}}{\sqrt{\left( {\sum\limits_{i = 1}^{N}A_{i}^{2}} \right)\left( {\sum\limits_{i = 1}^{N}B_{i}^{2}} \right)}}\frac{{N{\sum{A_{i}B_{i}}}} - {\sum{A_{i}{\sum B_{i}}}}}{\sqrt{{N{\sum A_{i}^{2}}} - \left( {\sum A_{i}} \right)^{2}}\sqrt{{N{\sum B_{i}^{2}}} - \left( {\sum B_{i}} \right)^{2}}}}$

where A_(i) is the intensity in the i^(th) voxel of image A, B_(i) isthe intensity in the corresponding i^(th) voxel of image B and N is thenumber of voxels considered, and the sum is taken from i equals one toN. It should be appreciated that the metric may be optimal when imagedifferences are minimized (or when the correlation of image similaritiesis maximized). The NC metric generally is insensitive to intensityshifts and to multiplicative factors between the two images and mayproduce a cost function with sharp peaks and well defined minima.

Finally, operation 428 averages the correlations computed in everybucket with weights proportional to the number of sample points in thebucket.

It is to be appreciated that the above process for computing the metricmay compensate for non-uniform intensities, for example, those describedabove with respect to FIGS. 3A-3C, in the MRI scan data.

During the registration process, an optimizer may be used to maximizeimage similarity between the reference image and target image byadjusting the parameters of a given transformation model to adjust thelocation of reference image coordinates in the target image. In oneembodiment, the optimizer for a registration operation may use thetransformed image (e.g., the transformed golden template) from theprevious registration operation as its initial approximation. Then,local optimization techniques may be used to search for a local optimumnear the initial starting approximation. This may be done so that anypotential matches farther away from the feature of interest (e.g., thefemur or tibia in a knee joint) reliably found in an earlier operationmay be eliminated.

For the translation transformation, an exhaustive search may beperformed using a grid 10×10×10 of size 5-millimeter translationvectors. A translation for every vector in the grid may be performed andthe translation providing a maximum local correlation in sample pointsmay be selected as the optimum translation.

For the similarity transformation, a regular step gradient descentoptimizer may be used by one embodiment. A regular step gradient descentoptimizer typically advances transformation parameters in the directionof the gradient and a bipartition scheme may be used to compute the stepsize. The gradient of a function typically points in the direction ofthe greatest rate of change and whose magnitude is equal to the greatestrate of change.

For example, the gradient for a three dimensional space may be given by:

${\nabla{f\left( {x,y,z} \right)}} = {\left( {\frac{\partial f}{\partial x},\frac{\partial f}{\partial y},\frac{\partial f}{\partial z}} \right).}$

That is, the gradient vector may be composed of partial derivatives ofthe metric function over all the parameters defining the transform. Inone embodiment the metric function may be a composition of an outer andN inner functions. The outer function may compute a metric valueaccording to operations 426 and 428 given the vectors {A_(i)} and{B_(i)}. The N inner functions may map N sample points from the fixed(reference) image A_(i) into the target image B_(i) using the transformand evaluate intensities of the target image B_(i) in the mapped points.Each of the inner functions generally depends on the transformparameters as well as on the point in the “from” space to which thetransform is applied. When computing the partial derivatives, the chainrule for computing a derivative of the function composition may be used.

To find a local minimum, parameter steps may be taken in the directionof the negative of the metric gradient (or the approximate gradient)over the transform parameter space at the current point. This generallyoptimizes the metric which typically has a local minimum when featuresof the reference image mapped into corresponding features of the targetimage have minimal misalignment).

The initial center of rotation for the similarity transformation (e.g.,operation 382 of FIG. 17) may be specified as the center of a boundingbox (or minimum sized cuboid with sides parallel to the coordinateplanes) that encloses the feature (e.g., a bone) registered in thetranslation registration (e.g., operation 380 of FIG. 17). Scalingcoefficients of approximately 40-millimeters may be used for the scalingparameters when bringing them together with translation parameters. Itis to be appreciated that the gradient computation generally relies on acustomized metric function in the parameter space, due to the fact thatwith a similarity transformation, the transform parameters do not havethe same dimensionality. The translation parameters have a dimension ofmillimeters, while the parameters for rotational angles and scaling donot have a dimension of millimeters. In one embodiment, a metric p maybe defined asρ=SQRT(X ² +Y ² +Z ²+(40-millimeter*A1)²+ . . . )

where X is the translation along the x axis, Y is the translation alongthe y axis, Z is the translation along the z axis, A1 is the firstrotation angle, etc. A scaling coefficient of approximately40-millimeters may be used because it is approximately half the size ofthe bone (in the anterior/posterior and medial/lateral directions) ofinterest and results in a point being moved approximately 40-millimeterswhen performing a rotation of one radian angle.

In one embodiment, a maximum move of 1.5-millimeters may be specifiedfor every point, a relaxation factor may be set to 0.98 and a maximum of300 iterations may be performed to determine the parameters of thesimilarity transformation that results in minimal misalignment betweenthe reference image and target MRI scan.

For the affine transformation, a regular step gradient optimizer may beused by one embodiment. Scaling coefficients of approximately40-millimeters may be used for the matrix coefficients variations whenbringing them together with translation parameters. A maximum1.0-millimeter move for every point may be set for each iteration, therelaxation factor may be set to 0.98 and a maximum of 300 iterations maybe performed to determine the parameters of the affine transformationthat results in minimal misalignment.

For the B-spline transformation, a modified regular step gradientdescent optimizer may be used by one embodiment when searching for thebest B-spline deformable transformation. An MRI image gradient may oftenfollow the bone surface in diseased areas (e.g., where the bone contactsurface is severely damaged and/or where osteophytes have grown). Such agradient may cause deformations of the golden template that wouldintroduce large distortions in the segmented bone shape.

In one embodiment, the MRI image gradient may be corrected for suchdeformations by computing a normal to golden boundary vector field whereevery vector points towards the closest point in the golden templateshape found during the affine transformation (e.g., operation 384 ofFIG. 17). This may be done using a distance map (also referred to as adistance transform). A distance map supplies each voxel of the imagewith the distance to the nearest obstacle voxel (e.g., a boundary voxelin a binary image). In one embodiment, the gradient of the signeddistance map of the golden tibia region may be mapped using the affinetransformation found in operation 384 of FIG. 17. In one embodiment, asigned Danielsson distance map image filter algorithm may be used. Then,the MRI image gradient may be projected onto the vector field to obtainthe corrected gradient field. This corrected gradient field is parallelto the normal to golden boundary field and typically defines a very thinsubset of the set of B-spline transformations that may be spanned duringthe optimization.

Additionally, rather than computing one gradient vector for thetransform space and taking a step along it, a separate gradient may becomputed for every spline node. In one embodiment, order three B-splines(with J×K×L control nodes) may be used and J×K×L gradients may becomputed, one for each control point. At every iteration, each of thespline nodes may be moved along its respective gradient. This may allowthe spline curve to be moved in low contrast areas at the same time itis moved in high contrast areas. A relaxation factor of 0.95 may be usedfor each spline node. A maximum move of one-millimeter may be set forevery point during an iteration and a maximum of 20 iterations may beperformed to find the parameters of the B-spline transformation thatprovides minimal misalignment of the golden tibia region mapped into thetarget MRI scan.

Once the position and shape of the feature of interest of the joint hasbeen determined using image registration (operation 370 of FIG. 16), theregistration results may be refined using anchor segmentation and anchorregions (operation 372 of FIG. 16). FIG. 21 depicts a flowchartillustrating one method for refining the registration results usinganchor segmentation and anchor regions. Typically, during thisoperation, one more registration may be done using an artificiallygenerated image for the fixed image 394 of the registration framework390. Use of an artificial image may improve the overall segmentation byregistering known good regions that typically do not change from scan toscan to correct for any errors due to diseased and/or low contrast areasthat otherwise may distort the registration.

Additionally, the artificial image may be used to increase surfacedetection precision of articular surfaces and shaft middle regions. Theimage slices typically have higher resolution in two dimensions (e.g.,0.3-millimeter in the y and z dimensions) and lower resolution in thethird dimension (e.g., 2-millimeters in the x dimension). The articularsurfaces and shaft middle regions typically are well defined in theimage slices due to these surfaces generally being perpendicular to theslices. The surface detection precision may be improved using acombination of 2D and 3D techniques that preserves the in-sliceprecision by only moving points within slices rather than betweenslices. Further, a 3D B-spline transform may be used such that theslices are not deformed independently of one another. Since each slicemay not contain enough information, deforming each slice independentlymay result in the registration finding the wrong features. Instead, theslices as a whole may be deformed such that the registration remainsnear the desired feature. While each slice may be deformed differently,the difference in deformation between slices generally is small suchthat the changes from one slice to the next are gradual.

In one embodiment, the artificial image may comprise a set of dark andlight sample points that may be used by the metric. All dark points inthe artificial image may have the same intensity value (e.g., 100) andall light points in the artificial image may have the same intensityvalue (e.g., 200). It should be appreciated that the correlations aregenerally insensitive to scaling and zero shift. Thus, any intensityvalues may be used as long as the dark intensity value is less than thelight intensity value.

Initially, operation 430 may apply the cumulative registration transform(computed by operation 370 of FIG. 16) to an anchor segmentation meshand its three associated anchor region meshes (e.g., InDark-OutLightmesh, InLight-OutDark mesh and Dark-in-Light mesh) to generate atransformed anchor segmentation mesh and associated transformed anchorregion meshes (transformed InDark-OutLight anchor mesh, transformedInLight-OutDark anchor mesh and transformed Dark-in-Light anchor mesh)that lie in a space identical to the target image space.

Then, operation 432 generates random sample points lying within a thinvolume surrounding the transformed anchor segmentation mesh surface. Inone embodiment, this may be a volume having an outer boundary defined bythe anchor segmentation mesh surface plus 1.5-millimeters and an innerboundary defined by the anchor segmentation mesh surface minus1.5-millimeters, which may be referred to herein as the 1.5-millimeterneighborhood. The random sample points may be generated such that theyare within the image slices of the target scan but not between theslices. For example, the image slices may be transversal to the x-axiswith a spacing of 2-millimeters (at x-axis locations 0.0, 2.0, 4.0, . .. ). When a sample point is selected, its x-coordinate may be one of0.0, 2.0, 4.0, etc. but may not be 1.7, 3.0, or some non-multiple of2.0.

In one embodiment, voxels may be marked in every image slice that belongto the 1.5-millimeter neighborhood as follows. First, the intersectionof the transformed anchor mesh with every image slice may be found. Itshould be appreciated that the intersection of the anchor mesh with animage slice may be a polyline(s). Then, in each image slice, thepolyline segments may be traversed and all pixels that intersect withthe mesh may be marked. Next, a Dilate filter may be applied to themarked pixels of each image slice using a radius of 1.5-millimeters. TheDilate filter typically enlarges the marked region by adding all thepoints that lie within a 1.5-millimeter distance from the originallymarked points.

After operation 432, operation 434 determines if a sample point liesinside the transformed InDark-OutLight mesh surface. If operation 434determines that the sample point lies inside the transformedInDark-OutLight mesh surface, then operation 442 is performed. Ifoperation 434 determines that the sample point does not lie inside thetransformed InDark-OutLight mesh surface, then operation 436 isperformed.

Operation 442 determines if the sample point lies inside the transformedanchor segmentation mesh surface. If operation 442 determines that thesample point lies inside the transformed anchor segmentation meshsurface, then operation 446 is performed. If operation 442 determinesthat the sample point does not lie inside the transformed anchorsegmentation mesh surface, then operation 448 is performed.

Operation 436 determines if the sample point lies inside the transformedInLight-OutDark mesh surface. If operation 436 determines that thesample point lies inside the transformed InLight-OutDark mesh surface,then operation 444 is performed. If operation 436 determines that thesample point does not lie inside the transformed InLight-OutDark meshsurface, then operation 438 is performed.

Operation 444 determines if the sample point lies inside the transformedanchor segmentation mesh surface. If operation 444 determines that thesample point lies inside the transformed anchor segmentation meshsurface, then operation 448 is performed. If operation 444 determinessample point does not lie within the transformed anchor segmentationmesh surface, then operation 446 is performed.

Operation 438 determines if the sample point lies inside the transformedDark-In-Light mesh surface. If operation 438 determines that the samplepoint lies inside the transformed Dark-In-Light mesh surface, thenoperation 440 is performed. If operation 438 determines that the samplepoint does not lie inside the transformed Dark-In-Light mesh surface,then operation 450 is performed.

Operation 440 determines if the sample point is within 0.75-millimeterof the surface of the transformed anchor segmentation mesh. If operation440 determines that the sample point is within 0.75-millimeter of thesurface of the transformed anchor segmentation mesh, then operation 446is performed. If operation 440 determines that the sample point is notwithin 0.75-millimeter of the surface of the anchor segmentation mesh,then operation 450 is performed.

Operation 446 adds the sample point to the artificial image as a darkpoint. Then, operation 450 is performed.

Operation 448 adds the sample point to the artificial image as a lightsample point. Then, operation 450 is performed.

Operation 450 determines if there are more randomly generated samplespoints to be added to the artificial image. If operation 450 determinesthat there are more randomly generated sample points to be added to theartificial image, then operation 434 is performed. If operation 450determines that there are no more randomly generated sample points to beadded to the artificial image, then operation 452 is performed.

FIG. 22 depicts a set of randomly generated light sample points 460 anddark sample points 462 over the target MRI 464. In one embodiment,approximately 8,000 sample points (light and dark) may be generated overthe entire artificial image.

Referring again to FIG. 21, if operation 450 determines that there areno more randomly generated sample points to be added to the artificialimage, operation 452 registers the set of dark and light points to thetarget MRI scan. This operation may perform a registration similar tothe registration operation 196 (depicted in FIG. 17). In thistransformation, a subset of B-spline deformable transformations may beperformed that move points along their respective slices, but nottransversal to their respective slices.

In a B-spline deformable transform, a translation vector for everycontrol point (e.g., in the set of J×K×L control points) may bespecified. To specify a transform that moves any point in 3D space alongthe y and z slice coordinates but not along the x coordinate, arestriction on the choice of translation vectors in the control pointsmay be introduced. In one embodiment, only translation vectors with thex coordinate set equal to zero may be used to move points in the planeof the slice (e.g., the y and z directions) but not transversal to theslice (e.g., the x direction).

The use of anchor region meshes which typically are well pronounced inmost image scans may reduce registration errors due to unhealthy areasand/or areas with minimal contrast differences between the feature to besegmented and surrounding image areas. For example, in the area where ahealthy bone normally has cartilage, a damaged bone may or may not havecartilage. If cartilage is present in this damaged bone region, the boneboundary separates the dark cortical bone from the gray cartilagematter. If cartilage is not present in this area of the damaged bone,there may be white liquid matter next to the dark cortical bone or theremay be another dark cortical bone next to the damage bone area. Thus,the interface from the cortical bone to the outside matter in thisregion of the damaged bone typically varies from MRI scan to MRI scan.In such areas, the interface between the cortical and the innercancellous bone may be used as an anchor region.

The use of a subset of B-Spline deformable transforms may reduce errorsdue to the 2-millimeter spacing between image slices.

FIG. 23 depicts a flowchart illustrating one method for generatingspline curves outlining the surface of a feature of interest in eachtarget MRI slice (e.g., operation 376 of FIG. 16). Initially, operation470 intersects the generated 3D mesh model of the feature surface with aslice of the target scan data. The intersection defines a polyline curveof the surface of the feature (e.g., bone) in each slice. Two or morepolyline curves may be generated in a slice when the bone is not verystraightly positioned with respect to the slice direction.

A polyline curve is a piecewise linear approximation to a curved featureshape. Generally, this curve should be easy to manipulate with a set ofcontrol points. The polyline curve may have many segments, making itmore difficult to manipulate the polyline curve (e.g., during operation254 or 260 of FIG. 6). One embodiment may generate one or more Kochaneksplines from the polyline curve. Each spline typically has a smallernumber of control points and typically fits the polyline curve withabout 0.2-millimeter deviation. Generally, a Kochanek spline may havemore control points along the high curvature regions of the polylinecurve and fewer control points along low curvature regions (i.e., wherethe curve tends to be flatter) of the polyline curve.

Once a polyline curve has been generated, operation 472 may compute apolyline parameterization, L_(i), as a function of the polyline'slength. FIG. 24 depicts a polyline curve 481 with n vertices, V₀, V₁, .. . V_(i−1), V_(i) . . . V_(n−1). Note that vertex V₀ follows vertexV_(n−1) to form a closed contour curve. The length of a segmentconnecting vertices Vi−1 and Vi may be denoted by ΔL_(i) such that thelength parameterization, L_(i), of the polyline at vertex V_(i) may beexpressed as:L _(i) =ΔL ₀ +ΔL ₁ + . . . +ΔL _(i).

Next, operation 474 may compute a polyline parameterization, A_(i), as afunction of the polyline's tangent variation. The absolute value of theangle between a vector connecting vertices V_(i−1) and V_(i) and avector connecting vertices V_(i) and V_(i+1) may be denoted by ΔA_(i)such that the tangent variation parameter A_(i) at vertex V_(i) may beexpressed as:A _(i) =ΔA ₀ +ΔA ₁ + . . . +ΔA _(i).

Then, operation 476 determines a weighted sum parameterization of thepolyline length and tangent variation parameterizations. In oneembodiment the weighted sum parameterization, W_(i), at vertex V_(i) maybe computed as:W _(i) =α*L _(i) +β*A _(i)where α may be set to 0.2 and β may be set to 0.8 in one embodiment.

Then, operation 478 may perform a uniform sampling of the polyline usingthe W parameterization results determined by operation 476. In oneembodiment, a spacing interval of approximately 3.7 of the W parametervalue may be used for positioning K new sample points. First, K may becomputed as follows:K=ROUND(W _(n)/3.7).

That is, the W parameter value, which is the last computed value W_(n),may be divided by 3.7 and the result rounded to the nearest integer toget the number of new sample points. Then, the spacing of the samplepoints, ΔW may be computed as:ΔW=W _(n) /K.

Finally, the K new sample points, which are uniformly spaced, may bepositioned at intervals ΔW of the parameter W. The resulting samplepoints may be used as control points for the Kochanek splines to convertthe polyline into a spline. A Kochanek spline generally has a tension, abias and a continuity parameter that may be used to change the behaviorof the tangents. That is, a closed Kochanek spline with K control pointstypically is interpolated with K curve segments. Each segment has astarting point, an ending point, a starting tangent and an endingtangent. Generally, the tension parameter changes the length of thetangent vectors, the bias parameter changes the direction of the tangentvectors and the continuity parameter changes the sharpness in changebetween tangents. In certain embodiments, the tension, bias andcontinuity parameters may be set to zero to generate a Catmull-Romspline.

In one embodiment, operation 478 may perform a linear interpolation ofW_(i) and W_(i+1) to locate a sample point that lies between W_(i) andW_(i+1). The interpolated value of W may be used to determine thecorresponding sample location in the segment connecting vertices V_(i)and V_(i+1).

In certain embodiments, operation 478 may divide the W parameter valueby six to obtain the new number of sample points K. That is,K=ROUND(W _(n)/6).

Then, a measure of closeness (i.e., how closely the spline follows thepolyline) may be computed as follows. First, the spline is sampled suchthat there are seven sample points in every arc of the spline (i.e., 7*Ksample points). Then, the sum of the squared distances of the samplepoints to the polyline may be computed. Next, the coordinates of the Kcontrol points are varied (i.e., two*K parameters). Then, a localoptimization algorithm is used to find the closest spline. If theclosest spline found during the optimization is not within a certainprecision (e.g., within approximately 0.4-millimeter of the polyline),then the number of control points, K, may be increased by one. The newnumber of control points may be uniformly distributed along the Wparameter, and another optimization performed to find the new closestspline. Generally one to two optimizations provide a spline that followsthe polyline with the desired degree of precision (e.g., withinapproximately 0.2-millimeter).

Finally, operation 480 determines if a spline curve(s) should begenerated for another image slice. If operation 480 determines that aspline curve should be generated for another slice, then operation 472is performed. If operation 480 determines that there are no more imageslices to be processed, the method terminates.

As discussed above, in one embodiment, the output of the segmentationmay be a triangular mesh (e.g., a 3D surface model) of the segmentedbone(s) of a joint (e.g., the femur and tibia of a knee joint). The meshgenerated generally represents a watertight surface that closely followsthe segmentation contour curves of the slices, smoothly interpolatesbetween the segmentation contour curves, and may have a low triangularcount.

In one embodiment, a triangular mesh may be generated as follows. Thesegmentation data may be represented in 3D using (x, y, z) coordinateswith the image slices transversal to the x direction. Thus, thesegmentation contours lie in yz planes with fixed values of x.Initially, an in-slice distance image may be computed for each segmentedslice. The value of each (y, z) pixel in an in-slice distance image isthe distance to the closest point in the contours when the point islocated inside one of the contours and is the inverse (i.e., negative)of the distance to the closest point in the contours when the point isoutside all of the contours.

Then, a marching cubes algorithm may be applied to the in-slice distanceimages to generate the mesh. The marching cubes algorithm is a computeralgorithm for extracting a polygonal mesh of an isosurface (i.e., thecontours) from a three-dimensional scalar field (or voxels). Thealgorithm typically proceeds through the voxels, taking eight neighborvoxels at a time (thus forming an imaginary cube) and determines thepolygon(s) needed to represent the part of the isosurface (i.e.,contour) that passes through the imaginary cube. The individual polygonsare then fused into the desired surface. The generated mesh generallypasses through the zero level of the signed distance function in eachslice such that the mesh lies close to the contours.

It is to be appreciated that the image resolution in the y and zdirections typically determines how well the zero level of the signeddistance function approximates the original contours and may alsodetermine the triangular count in the resulting mesh. In one embodiment,a voxel size of 1.5-millimeters in the y and z directions may be used.This typically yields deviations within 0.1-millimeter of the originalcontours and produces a smooth mesh.

In one embodiment, a smoothing operation may be performed in the xdirection (i.e., transversal to the image slices) to compensate forsurface waviness that may have been introduced when the automaticallygenerated contours were adjusted (e.g., during operation 260 of FIG. 6).Such waviness may occur in regions of an image slice where there isminimal contrast variation and the curve is positioned by thetechnician. Typically a smooth best guess mesh in uncertain areas may bedesired when generating a planning model that may be used to locate theposition of an implant. Alternatively, a smooth overestimation may bedesired in uncertain areas such as in an arthritic model used to createa jig.

In one embodiment, simple smoothing may be used and the amount ofsmoothing (i.e., how much a voxel value may be modified) may becontrolled by two user specified parameters, MaxUp and MaxDown. After anaverage is computed for a voxel, it is clamped using these values tolimit the amount of smoothing. The smoothing operation typically doesnot change the image much in areas where the image contrast is good. Forsmooth best guess averaging in uncertain areas, MaxUp and MaxDown mayeach be set to 1 millimeter. For smooth overestimation averaging inuncertain regions, MaxUp may be set to 2-millimeters and MaxDown may beset to 0-millimeter.

The operation of adjusting segments of the segmentation process will nowbe described with reference to FIG. 25, which depicts a flowchart forone method of adjusting segments (e.g., operation 254 or operation 260of the flowchart depicted in FIG. 6). In one embodiment, thesegmentation data may be manually adjusted by a trained techniciansitting in front of a computer 6 and visually observing theautomatically generated contour curves in the image slices on a computerscreen 9. By interacting with computer controls 11, the trainedtechnician may manually manipulate the contour curves. The trainedtechnician may visually observe all of the contours as a 3D surfacemodel to select an image slice for further examination.

Initially, in operation 482 a slice is selected for verification. In oneembodiment, the slice may be manually selected by a technician.

Next, operation 484 determines if the segmentation contour curve in theselected slice is good. If operation 484 determines that thesegmentation contour curve is good, then operation 494 is performed. Ifoperation 484 determines that the segmentation contour curve is notgood, then operation 486 is performed.

Operation 486 determines if the segmentation contour curve isapproximately correct. If operation 486 determines that the contourcurve is approximately correct, then operation 492 is performed.

In operation 492 incorrect points of the segmentation contour curve maybe repositioned. In one embodiment this may be performed manually by atrained technician. It is to be appreciated that it may be difficult forthe technician to determine where the correct contour curve should belocated in a particular slice. This may be due to missing or unclearbone boundaries and/or areas with little contrast to distinguish imagefeatures. In one embodiment, a compare function may be provided to allowthe technician to visually compare the contour curve in the currentslice with the contour curves in adjacent slices. FIG. 26 depicts animage showing the contour curve 510 (e.g., a spline curve) with controlpoints 512 of the contour curve 510 for the current image slice as wellthe contour curves 514, 516 of the previous and next image slices,respectively, superimposed on the current image slice.

It may be difficult to determine where the correct segmentation contourcurve should be located due to missing or unclear bone boundaries due tothe presence of unhealthy areas, areas with limited contrastdifferences, and/or voxel volume averaging. When visually comparingadjacent slices, the technician may visualize the data in 2D planes (xy,yz, and xz) and in 3D. In one embodiment, the technician may select anarea for examination by positioning a crosshair on a location in anywindow and clicking a mouse button to select that image point. Thecrosshair will be placed at the desired point and may be used toindicate the same location when the data is visualized in each window.

The technician may use the spline control points to manipulate the shapeof the curve. This may be done by using a mouse to click on a controlpoint and dragging it to a desired location. Additionally, thetechnician may add or delete spline curve control points. This may bedone by using a mouse to select two existing control points betweenwhich a control point will be inserted or deleted. Alternatively, thetechnician may use a mouse cursor to point to the location on the curvewhere a control point is to be inserted. In one embodiment, by pressingthe letter I on a keyboard and then positioning the cursor at thedesired location, clicking the left mouse button will insert the controlpoint. A control point may be deleted by pressing the letter D on thekeyboard and then positioning the cursor over the desired control pointto be deleted. The selected control point will change color. Theselected control point will be deleted when the left mouse button isclicked.

Referring again to FIG. 25, if operation 486 determines that the contourcurve is not approximately correct, operation 488 is performed to deletethe curve. Then, operation 490 is performed.

Operation 490 generates a new segmentation contour curve for the imageslice. In one embodiment, a technician may use a spline draw tool toinsert a new spline curve. With the spline draw tool, the technician mayclick on consecutive points in the current slice to indicate where thespline curve should be located and a spline curve is generated thatpasses through all of the indicated points. A right mouse click may beused to connect the first and last points of the new spline curve.Alternatively, the technician may use a paste command to copy the splinecurve(s) from the previous slice into the current slice. The splinecontrol points may then be manipulated to adjust the spline curves tofollow the feature in the current image slice.

In another embodiment, a paste similar command may be used by thetechnician to copy the spline curve from the previous slice into thecurrent slice. Rather than pasting a copy of the spline curve from theprevious slice, the spline curve may be automatically modified to passthrough similar image features present in both slices. This may be doneby registering a region around the spline curve in the previous slicethat is from about 0.7-millimeter outside of the curve to about5.0-millimeter within the curve. Initially, this region is registeredusing an affine transformation. Then, the result of the affine transformmay be used as a starting value for a B-Spline deformabletransformation. The metric used for the transform may be the localcorrelation in sample points metric described previously. Typically,more sample points may be taken closer to the curve and fewer samplepoints taken farther away from the curve. Next, the spline controlpoints may be modified by applying the final transformation found to thespline control points. Additionally, the trained technician may adjustfrom zero to a few control points in areas where the bone boundarychanges a lot from the slice due to the bone being tangent to the sliceor in areas of limited contrast (e.g., where there is an osteophytegrowth). Then, operation 492 is performed.

Operation 494 determines if there are additional slices to be verified.If operation 494 determines that there are additional slices to beverified, operation 482 is performed.

If operation 494 determines that there are no more slices to beverified, then operation 496 is performed. Operation 496 generates a 3Dsurface model of the segmented bone.

Then, operation 498 determines if the 3D surface model is good. In oneembodiment, a technician may manually determine if the 3D surface modelis good. The technician may use a spline 3D visualization tool thatgenerates a slice visualization showing the voxels inside all of thesplines in 3D, as illustrated by the 3D shape 520 depicted in FIG. 27.This spline 3D visualization tool typically may be generated in realtime to provide interactive updates to the technician as the splinecurves are manually edited. Alternatively, a mesh visualization may begenerated in response to a technician command. The mesh visualizationtypically generates a smooth mesh that passes close to all the splinecurves, e.g., mesh 290 depicted in FIG. 9.

If operation 498 determines that the 3D model is not good, thenoperation 500 is performed. Operation 500 selects a slice lying in anarea where the 3D shape is not good. In one embodiment, a technician maymanually select the slice. Then, operation 482 is performed.

If operation 498 determines that the 3D model is good, then the methodterminates.

The 3D surface models of the lower end of the femur and the upper end ofthe tibia of a patient's knee may be used to create arthroplasty jigsand/or implants. For example, the models may be used to create femur andtibia jigs that can be used with a patient's femur and tibia asdisclosed in the various U.S. Patent Applications incorporated byreference herein in this Detailed Description and filed by Park and Parket al. Automatic segmentation of image data to generate 3D bone modelsmay reduce the overall time required to perform a reconstructive surgeryto repair a dysfunctional joint and may also provide improved patientoutcomes.

B. Segmentation Using Landmarks of Scanner Modality Image Data toGenerate 3D Surface Model of a Patient's Bone

Now begins a discussion of an alternative embodiment of imagesegmentation. The alternative embodiment includes placing landmarks 777(in FIG. 35A-FIG. 35H) on image contours. The landmarks 777 are thenused to modify a golden bone model (e.g., golden femur or golden tibia),the resulting modified golden bone model being the output ofsegmentation.

Similar to the embodiment of image segmentation discussed above insection b. of this Detailed Discussion, in one version of thealternative embodiment of image segmentation, the 2D images 16 of thepatient's joint 14 are generated via the imaging system 8 (see FIG. 1Aand [block 100] of FIG. 1B). These images 16 are analyzed to identifythe contour lines of the bones and/or cartilage surfaces that are ofsignificance with respect to generating 3D models 22, 36, as discussedabove in section a. of this Detailed Discussion with respect to [blocks110 and 130] of FIGS. 1C and 1D. Specifically, a variety of imagesegmentation processes may occur with respect to the 2D images 16 andthe data associated with such 2D images 16 to identify contour linesthat are then compiled into 3D bone models, such as bone models 22,restored bone models 28, and arthritic models 36.

Algorithms and software are described in this Detailed Discussion forautomatic and semi-automatic image segmentation. In the DetailedDescription, alternative software tools and underlying methods aredescribed, such alternative tools and methods helping a user to quicklygenerate bone models. Because the alternative software requires someuser input such as, for example, initial Landmark positions, finalverification and, in some instances, adjustment, this alternativesegmentation process can be considered a semi-automatic segmentationprocess.

In some cases the alternative embodiment described in section c. of thisDetailed Discussion may significantly reduce the user time spent onsegmentation. In particular, compared to manual segmentation (where theuser draws contour(s) by hand on each applicable slice for eachapplicable bone), the user time may be reduced by approximately fivetimes when a user segments a planning model intended for communicating apreoperative planning model to a surgeon. For that purpose, a user maygenerate 3D bone models with high precision in particular areas and lessprecision in other areas. In some implementations, a user may get highprecision (e.g., 0.5 mm) at well-defined bone contours in MRI imagesoutside the implant regions and less precision (e.g., up to 2 mm) in theregions that will be replaced with implants by spending approximately3-4 minutes in the user interface (“UI”) setting landmarks for thealgorithm. If improved precision is desired, the user may position morelandmarks and thus spend more time in the UI.

In one embodiment, the software tool described in section c. of theDetailed Discussion is called “Segmentation using Landmarks”. This toolmay be implemented inside software application PerForm 1.0. A variety ofprocesses and methods for performing image segmentation using landmarksare disclosed herein.

The imager 8 typically generates a plurality of image slices 16 viarepetitive imaging operations. Depending on whether the imager 8 is aMRI or CT imager, each image slice will be a MRI or CT slice. As shownin FIG. 2A, the image slice may depict the cancellous bone 200, thecortical bone 202 surrounding the cancellous bone, and the articularcartilage lining portions of the cortical bone 202 of an object ofinterest of a joint, e.g., a femur 204 in a patient's knee joint 14. Theimage may further depict the cancellous bone 206, the cortical bone 208of another object of interest in the joint, e.g., a tibia 210 of theknee joint 14. In one embodiment, each image slice 16 may be atwo-millimeter 2D image slice.

One embodiment may segment one or more features of interest (e.g.,bones) present in MRI or CT scans of a patient joint, e.g., knee, hip,elbow, etc. A typical scan of a knee joint may represent approximately a100-millimeter by 150-millimeter by 150-millimeter volume of the jointand may include about 40 to 80 slices taken in sagittal planes. Asagittal plane is an imaginary plane that travels from the top to thebottom of the object (e.g., the human body), dividing it into medial andlateral portions. It is to be appreciated that a large inter-slicespacing may result in voxels (volume elements) with aspect ratios ofabout one to seven between the resolution in the sagittal plane (e.g.,the y z plane) and the resolution along the x axis (i.e., each scanslice lies in the yz plane with a fixed value of x). For example, atwo-millimeter slice that is 150-millimeters by 150-millimeters may becomprised of voxels that are approximately 0.3-millimeter by0.3-millimeter by 2-millimeters (for a 512 by 512 image resolution inthe sagittal plane).

In one embodiment, each slice may be a gray scale image with aresolution of 512 by 512 voxels where the voxel value represents thebrightness (intensity) of the voxel. The intensity may be stored as a16-bit integer resulting in an intensity range from 0 to 65,535, where 0may represent black and 65,535 may represent white. The intensity ofeach voxel typically represents the average intensity of the voxelvolume. Other embodiments may employ scans having higher or lowerresolutions in the sagittal plane, different inter-slice spacing, orimages where the intensity may be represented by a 24 bit vector (e.g.,eight bits each for a red component, green component and bluecomponent). Additionally, other embodiments may store intensity valuesas 8-bit or 32-bit signed or unsigned integers or floating point values.

Typical MRI and CT scan data generally provide images where parts of abone boundary of interest may be well defined while other parts of thebone boundary may be difficult to determine due to voxel volumeaveraging, the presence of osteophyte growth, the presence of tissuehaving similar image intensities in neighboring areas to the object tobe segmented, amongst other things. Such poor definition of parts of thebone boundary in the images may cause fully automated segmentationtechniques to fail. For example, FIG. 2A depicts regions 212 within aslice where an object boundary may not be visible due to neighboringtissue having about the same intensity as the feature of interest.Depicted in FIG. 2B are regions 214 that may be extended into the slicefrom adjacent slices due to a high voxel aspect ratio. Depicted in FIG.2C is a region 216 of the bone boundary 218 that may disappear or loseregularity when the bone boundary 218 is approximately tangent to theslice.

In one embodiment, a user may provide some additional input to theauto-segmentation algorithm, and the algorithm could use the additionaluser input for more accurate and faster segmentation of features ofinterest. For example, the additional user input may be a set of pointson the boundary of the feature of interest. In the context of a kneeprocedure, the points might be on the Femur knee bone boundary or on theTibia knee bone boundary. These can be called landmark points or simplylandmarks 777.

In order for a user to provide particular landmark points, the softwaremay allow loading MRI or CT image data, viewing and scrolling over imageslices, specifying landmark points in the slices and editing them. Thesoftware may also allow visualization of the segmentation results (i.e.,segmentation curves drawn in the image slices). The software may alsogenerate a 3D model from 2D outlining curves in 2D slices.

In one embodiment, PerForm software may be used to provide functionalityfor loading MRI or CT scanned data, visualizing sagittal, coronal andaxial slices and scrolling over them, drawing spline curves in slices,and generating a 3D mesh model passing through a set of spline curves.In one embodiment, a 3D mesh typically is a collection of vertices,edges, and faces that may define the surface of a 3D object. The facesmay consist of triangles, quadrilaterals or other simple convexpolygons. It should be appreciated that any other curve types may beemployed instead of spline curves. For example, polyline curves may beused.

In one embodiment, a tool called “Segmentation using Landmarks” is addedto PerForm software. Such a tool provides a UI for landmarks positioningand editing. The tool also provides a button “Segment”, which invokesthe segmentation algorithm. The algorithm uses 3D image and landmarksand generates spline curves outlining the required bone.

To begin the detailed discussion of the alternative embodiment of imagesegmentation described in this section c. of the Detailed Description,wherein landmarks 777 placed on image contours are used to modify agolden bone model (e.g., golden femur or golden tibia), the resultingmodified golden bone model being segmented, reference is made to FIG.28, which is a diagram depicting types of data employed in the imagesegmentation algorithm that uses landmarks. As shown in FIG. 28, thedata employed in the segmentation algorithm 600 may be characterized asbeing two types of data. The first type of data exists in the systemonce generated and is for use with multiple patients and is notgenerated specifically for the patient for which the current imagesegmentation is being undertaken. This type of data may be called goldenmodel data 602 and is derived similar to as discussed above with respectto FIG. 11, etc. and as generally reiterated below. The golden modeldata 602 may include, for example, one or more golden femur models 603and one or more golden tibia models 604. If the joint being treated issomething other than a knee, for example, the patient's arm, then thegolden model data 602 may include another type of golden bone model, forexample, a golden radius or golden ulna.

The second type of data is specific to the patient for which the currentimage segmentation is being undertaken. This type of data may be calledinput data for segmentation algorithm 606. The input data 606 includes3D image slices data 608, which is 3D image slice data of the patientbone via MRI, CT or another type of medical imaging. The input data 606also includes landmark data 610, which is landmarks 777 positioned onboundaries of the patient bone in the image slices. The input data 606further includes patient bone characteristics 612 such as bone type(e.g., whether the bone is a tibia or femur), bone right or lefthandedness, and whether the segmentation is being done to generate anarthritic model 36 (see FIG. 1D) or a planning or restored bone model 28(see FIG. 1C). As explained below, the golden model data 602 and theinput data 606 are used in the segmentation algorithm 600 to segment the3D image employing landmarks 777.

As shown in FIG. 29, which is a flowchart illustrating the overallprocess for generating a golden femur model 603 of FIG. 28, golden femurscan image slices 616 are obtained in operation 750. For example, asdiscussed above with respect to FIG. 11 above, a representative femur618 that is free of damage and disease may be scanned via medicalimaging, such as, for example, MRI or CT. Where the golden femur model603 is to be employed in generating a bone model 22 (see block 110 ofFIG. 1C) and cartilage geometry is not of interest, the golden femurscan images slices 616 may be of a femur having damaged cartilage aslong as the bone shape is otherwise desirable (e.g., normal) and free ofdeterioration or damage. Where the golden femur model 603 is to beemployed in generating an arthritic model 36 (see block 130 of FIG. 1D)and cartilage geometry is of interest, the golden femur scan imagesslices 616 may be of a femur having both cartilage and bone shape thatare desirable (e.g., normal) and free of deterioration or damage.

The appropriate femur scan may be selected by screening multiple MRIfemur scans to locate an MRI femur scan having a femur that does nothave damaged cancellous and cortical matter (i.e., no damage in femurregions that should be present in this particular model), which has goodMRI image quality, and which has a relatively average shape, e.g., theshaft width relative to the largest part is not out of proportion (whichmay be estimated by eye-balling the images). This femur scan data,referred to herein as a golden femur scan, may be used to create agolden femur template.

It is to be appreciated that several MRI scans of a femur (or other boneof interest) may be selected, a template generated for each scan,statistics gathered on the success rate when using each template tosegment target MRI scans, and selecting the one with the highest successrate as the golden femur template.

In other embodiments, a catalog of golden models may be generated forany given feature, with distinct variants of the feature depending onvarious patient attributes, such as (but not limited to) weight, height,race, gender, age, and diagnosed disease condition. The appropriategolden mesh would then be selected for each feature based on a givenpatient's characteristics.

In operation 752 and as indicated in FIG. 30, each of the image slices616 of the representative femur 618 are segmented with a contour curveor spline 620 having control points 622 and in a manner similar to thatdiscussed above with respect to FIG. 12A, etc. For example and as shownin FIG. 30, where the golden model is to be used in the generation of abone model 22 (see block 110 of FIG. 1C) and cartilage geometry is notof interest, each segmentation region includes cancellous matter andcortical matter of the femur in a manner similar to that discussed abovewith respect to the cancellous matter 322 and cortical matter 324 of thetibia depicted in FIG. 12A, etc. Thus, as shown in FIG. 30, the contourcurve 620 excludes any cartilage matter in outlining a golden femurregion.

On the other hand, where the golden model is to be used in thegeneration of an arthritic model 36 (see block 130 of FIG. 1D) andcartilage geometry is of interest, each segmentation region the contourcurve would include cartilage matter in outlining a golden femur region.

If the golden femur scan does not contain a sufficiently long shaft ofthe femur bone (e.g., it may be desired to segment a femur in a targetMRI that may have a longer shaft), then the image segmentation can beextrapolated beyond the image to approximate a normal bone shape. Thiscan be done because the femoral shaft is quite straight and, generally,all that is needed is to continue the straight lines beyond the MRIimage, as can be understood from the extension of the contour line 620proximal of the proximal edge of the femur image 616 of FIG. 30.

In operation 754 and as illustrated in FIG. 31A, the contour curves orsplines 620 are compiled and smoothed into a golden femur mesh 624 asdiscussed above with respect to FIG. 13A, etc. As indicated in FIG. 30,in one embodiment, the segmentation curve 620 is a closed curve. Thus,the resulting golden femur mesh 624 is a closed mesh as depicted in FIG.31A.

In operation 756 and as shown in FIG. 31B, the golden femur mesh 624 isconverted into an open golden femur mesh 626, wherein the proximalportion of the golden femur mesh 624 is removed to create the opensurface model called the open golden femur mesh 626. In other words, inoperation 756 the artificial part of the femur mesh 626 is cut off,namely the proximally extending shaft portion that results from theproximal extrapolated extension of the contour line 620, so as to obtainthe open golden femur mesh 626 of FIG. 31B.

In operation 758 and as indicated in FIG. 31C, regions 628, 629 of adifferent precision are generated for the golden femur mesh 626. Forexample, when segmenting the image slices 16 for the purpose ofgenerating a golden femur mesh 626 that is used to create a 3D computergenerated bone model used to show the preoperative planning (“POP”)images to a surgeon, it is desirable that the bone geometry of the mesh626 be generated with a relatively high degree of accuracy in certainregions 628 of the mesh 626 such that the resulting 3D computergenerated bone model allows the physician to verify the POP with adesired degree of accuracy, while other regions 629 of the mesh 626 maynot be generated to such a high degree of accuracy. For example, such adegree of accuracy in the certain regions 628 of the mesh 626 can beachieved via relatively precise image segmentation. The certain regions628 of the mesh 626 having the relatively high degree of accuracy couldinclude, among others, the lower shaft area, as depicted in FIG. 31C. Inone embodiment, the relatively high accuracy of the certain regions 628of the mesh 626 should allow the physician to verify the POP within 0.5mm accuracy.

As can be understood from FIG. 31C, in one embodiment, the highprecision region(s) 628 of the mesh 626 represent a portion of thedistal anterior femoral shaft that would be contacted by the anteriorflange of a candidate femoral implant. The rest of the mesh 626 may formthe region 629 that has an accuracy that is not as precise as the highprecision region 628. Such a lower precision region 629 of the mesh 626may include the entire distal femur excluding the distal anterior regionof the shaft included within the high precision region 628. Where thegolden femur mesh 626 is employed to form other 3D computer generatedbone models, such as, for example, the bone model 22 or arthritic model36, the mesh 626 may have a different number of high precision regions628 (e.g., none, one, two, three, or more such regions 628). Also, suchregions 628 may have precisions that are greater or less than statedabove. Finally, such regions 628 may correspond to different regions ofthe bone, encompass generally the entirety of the mesh surface, orinclude other regions in addition to the region 628 depicted in FIG.31C.

While the preceding discussion regarding the open golden bone mesh isgiven in the context of the open golden bone mesh being an open goldenfemur mesh 626, as can be understood from FIG. 32A-B, the open goldenbone mesh may be an open golden tibia mesh 630 having regions 632, 633of a different precision, all of which are generated in a manner similarto that discussed with respect to FIGS. 28-31C above.

For example, as can be understood from FIGS. 32A-32B, in one embodiment,the high precision region(s) 632 of the open golden tibia mesh 630represent a portion of the proximal anterior tibial shaft immediatelydistal the tibial plateau and running medial to lateral generallyproximal the tibial tuberosity. Another high precision region 632 mayoccupy a space similar in location and size, except on the posterior ofthe tibial shaft. The rest of the mesh 630 may form the region 633 thathas an accuracy that is not as precise as the high precision region 632.Such a lower precision region 633 of the mesh 630 may include the entireproximal tibia excluding the regions of the shaft included within thehigh precision regions 632. Where the golden tibia mesh 630 is employedto form other 3D computer generated bone models, such as, for example,the bone model 22 or arthritic model 36, the mesh 630 may have adifferent number of high precision regions 632 (e.g., none, one, two,three, or more such regions 632). Also, such regions 630 may haveprecisions that are greater or less than stated above. Finally, suchregions 630 may correspond to different regions of the bone, encompassgenerally the entirety of the mesh surface, or include other regions inaddition to the regions 632 depicted in FIGS. 32A-32B.

For a discussion of an alternative embodiment of operations 250-254 ofFIG. 6, reference is first made to FIG. 33, which is a flowchartillustrating the alternative embodiment of segmenting a target bone. Inthis example, the target bone is a femur 204, but may be a tibia 210 orany other type of bone.

As indicated in FIG. 33, operation 250 obtains or, more specifically,loads the scan data (e.g., scan images 16) generated by imager 8 of thepatient's joint 14 to be analyzed. In operation 251 the landmarks arepositioned in the scan images. In other words, as can be understood fromFIG. 34, which is a flowchart illustrating the steps of operation 251,operation 251 begins with operation 251 a, wherein the images 16 arescrolled through (e.g., medial to lateral or lateral to medial) to themost medial or lateral image slice were the femur bone 204 firstappears, as shown in FIG. 35A, which, in this example, is a most lateralsagittal MRI image slice 16 where the femur bone 204 or, morespecifically, the lateral epicondyle 776 first appears. Since the slice16 of FIG. 35A is the most lateral image where bone has begun to appear,the fibula 775 can be seen adjacent the tibia 210 in such instanceswhere the image slice is positioned so as to show both the femur 204 andthe tibia 210. In operation 251 b, two or more landmarks 777 arepositioned on the outer rim of the black cortical bone 208 of the imageslice 16 depicted in FIG. 35A. As is the case with all of the imagesdepicted in FIGS. 35A-35H, in one embodiment, the landmarks are placedvia an operator sitting at a work station. In one embodiment, theoperator or user is able to add landmarks by simply clicking onto theslice image, the landmark (point) being created at the exact coordinateswhere the click has occurred. The operator is able to move existinglandmarks within the slice by selecting them and moving them with themouse, a keyboard, a pen-and-tablet system, or similar. The user is ableto delete existing landmarks by selecting them and indicating to thesoftware that they should be deleted.

In another embodiment, a touch-screen surface may be used to provideinput and display for interactive editing of landmarks and segmentationcurves. Specialized gestures may be adopted for various editingoperations.

In another embodiment, a spatial input device may be used formanipulation of landmarks, segmentation curves, and other operationsinvolving POP and jig design activities.

In operation 251 c, the image slices 16 are scrolled lateral to medialthrough approximately three slices 16 further to a new image slice 16and, at operation 251 d, it is determined if the femur bone 204 is stillvisible in the new image slice 16, which is depicted in FIG. 35B. If so,then operation 251 e adds landmarks 777 to the new image slice 16 asindicated in FIG. 35B. Specifically, as indicated in FIG. 35B, this newimage slice 16 may show the femur lateral condyle 778 and be the firstimage slice having a clear boundary 779 of the femur lateral condyle. Ascan be seen in FIG. 35B, the fibula 775 and tibia 210 are also morefully shown. Landmarks 777 are set on the clear boundary 779 of theouter rim of the dark cortical bone of the femur lateral condyle, and anadditional landmark 777 is set on the opposite side 780 on the rim ofthe black cortical bone 208. As is the case with the placement oflandmarks 777 in any of the images 16, more or fewer landmarks 777 maybe placed along the rim of the black cortical bone depicted in the image16, including landmarks being placed on the rim of the black corticalcone of the entirety of the distal femur, including the distal femurcondyle and distal femur shaft.

Operations 251 c through 251 e are repeated to set landmarks 777 at thebone contour boundaries of approximately every third image slice 16moving lateral to medial until eventually at operation 251 d it isdetermined that bone no longer appears in the present image slice. Thus,as operation 251 of FIG. 33 loops through operations 251 c-251 e of FIG.34, landmarks 777 are set at the bone contour boundaries in each of thesagittal image slices 16 depicted in FIGS. 35C-35H, which are,respectively, approximately every third sagittal image slice 16 tabbinglateral to medial through all the sagittal image slices 16 loaded inoperation 250 of FIG. 33. Thus, as shown in FIG. 35C, which represents asagittal image slice 16 approximately three slices more medial than theimage slice 16 of FIG. 35B, the femur lateral condyle 778 has a clearbone contour boundary 779, and landmarks 777 are set along the boundary779 on the rim of the dark cortical bone 208. A landmark 777 is also seton the top region 780 of the cortical bone boundary 779 where the bonecontour boundary is less clear, the landmark being positioned on the rimof the dark cortical bone 208.

As illustrated in FIG. 35D, which represents a sagittal image slice 16approximately three slices 16 more medial than the image slice 16 ofFIG. 35C, the femur shaft 781 has now appeared in an image slice 16 andboth the femur shaft 781 and femur lateral condyle 778 have clear bonecontour boundaries 779. Landmarks 777 are set along the bone contourboundaries 779 on the rim of the dark cortical bone 208.

As shown in FIG. 35E, which represents a sagittal image slice 16approximately three slices 16 more medial than the image slice 16 ofFIG. 35D, the femur lateral condyle 778 is starting to disappear, andpart of its cortical bone contour boundary 779 is not clear. Landmarks777 are only set outside the dark cortical bone 208 in the regions wherethe contour boundary 779 is clear.

As illustrated in FIG. 35F, which represents a sagittal image slice 16approximately three slices 16 more medial than the image slice 16 ofFIG. 35E, the bone contour boundary 779 has become less clear as thefemur lateral condyle 778 has decreased in size as compared to the femurlateral condyle 778 of slice 16 in FIG. 35E. The slice 16 of FIG. 30F isjust lateral of the trochlear groove 782 between the femur lateralcondyle 778 and femur medial condyle 783. The bone contour boundary 779is clear in the anterior region of the femur lateral condyle 778 and twolandmarks 777 are placed there. Additional landmarks 777 are set alongthe bone contour boundaries 779 on the rim of the dark cortical bone208.

As indicated in FIG. 35G, which represents a sagittal image slice 16approximately three slices 16 more medial than the image slice 16 ofFIG. 35F, landmarks 777 are set along the bone contour boundaries 779 onthe rim of the dark cortical bone 208. The slice 16 of FIG. 35G is inthe trochlear groove 782 between the femur lateral condyle 778 and femurmedial condyle 783. The intercondylar eminence 784 of the tibia 210 canbe seen in the slice 16 of FIG. 35G.

As indicated in FIG. 35H, which represents a sagittal image slice 16approximately three slices 16 more medial than the image slice 16 ofFIG. 35G, the femur shaft 781 has begun to disappear and the femurmedial condyle 783 has begun to appear as the slice of FIG. 35H ismedial of the trochlear groove 782 depicted in the slice of FIG. 35G.The bone contour boundary 779 is clear in the anterior region of thefemur medial condyle 783 and two landmarks 777 are placed there.Additional landmarks 777 are set along the bone contour boundaries 779on the rim of the dark cortical bone 208.

As stated above, operations 251 c through 251 e continue to be repeatedas the slices 16 continue to be tabbed through lateral to medial to setlandmarks 777 at the bone contour boundaries of approximately everythird image slice 16 until eventually at operation 251 d it isdetermined that bone no longer appears in the present image slice.Operation 251 f then scrolls medial to lateral through the image slices16 until arriving at the image slice 16 where the most medial portion ofthe femur is depicted. Operation 251 g then sets two or more landmarks777 around the bone (e.g., the medial epicondyle) in a manner similar tothat depicted in FIG. 35A with respect to the lateral epicondyle 776.This is the end of operation 251 and, as can be understood from FIG. 33,operation 252 begins by pressing the “segment” button (operation 252 a),which causes segmentation lines to be generated for each slice 16 withlandmarks 777 (operation 252 b) in a manner similar to that illustratedand discussed above with respect to FIGS. 7A-7K or as now will bediscussed below beginning with FIG. 36.

When positioning landmarks, a user needs to distribute them over thecortical bone outer surface, favoring areas where the cortical boneboundary is sharp and is more orthogonal to the slice plane,particularly favoring certain “important” areas of the bone surface(where importance is dictated by eventual contact between bone andimplant or by other requirements from POP procedure.) The user shouldonly sparsely mark up the remaining parts of the bone, particularlywhere there is a lot of volume averaging (and/or the bone surface ismore parallel to slice plane.) While the image slices depicted in FIGS.35A-35H are MRI generated image slices, in other embodiments the imagingslices may be via other medical imaging methods, such as, for example,CT.

In one embodiment, the landmark-driven segmentation algorithm describedbelow is deliberately sensitive to the number of landmarks (points)placed at a given area of the bone. So for instance, if the user desiresthe auto-generated bone mesh to very accurately pass through particularspots on the slice, the user can place more than one landmark on thatsame spot or very near that spot. When there is a high concentration oflandmarks in a small area of the bone, the auto-generated mesh will bebiased to more accurately model that area. The software indicates to theuser, making it visible at a glance whenever more than one landmark islocated within the same small area of the image.

In one embodiment, instead of putting landmarks in every three slices, auser may position landmarks in every slice but use three times fewerlandmarks in each slice. The result of the segmentation usually variesvery little depending on how a user distributes landmarks around thebone surface as long as the entire surface is covered.

While much of the following discussion takes place in reference to thesegmentation of a femur (operation 252 of FIG. 6), the conceptsdiscussed herein are readily applicable to the segmentation of a tibia(operation 258 of FIG. 6). Additionally, the concepts discussed hereinare readily applicable to both the left or right knee. Different goldentemplate data may be used to segment the left tibia, right tibia, leftfemur or right femur for bone models 22 or planning models 28.Additionally, other embodiments may segment other models and or joints,including but not limited to, arthritic models 36, hip joints, elbowjoints, etc. by using an appropriate golden template of the feature ofinterest to be segmented.

As shown in FIG. 36, which is a flowchart illustrating the process ofsegmenting the target images 16 that were provided with landmarks 777 inoperation 251, the full or entire golden femur mesh 626, including itsregions 628, 629 in FIG. 31C, is deformed in operation 770 to matchlandmarks 777 and appropriate features, such as, for example, the outeredges of dark cortical bone, in the target scan images 16.

As discussed below with respect to FIG. 37, a method is provided formapping the golden femur mesh into the target scan using registrationtechniques. Registration may be thought of as an optimization problemwith a goal of finding a spatial mapping that aligns a fixed object witha target object. Generally, several registration operations may beperformed, first starting with a low-dimensional transformation group tofind a rough approximation of the actual femur location and shape in thetarget image. This may be done to reduce the chance of finding wrongfeatures instead of the femur of interest. For example, if a free-formdeformation registration was initially used to register the golden femurmesh to the target scan data, the template might be registered to thewrong feature, e.g., to a tibia rather than the femur of interest. Acoarse registration may also be performed in less time than a fineregistration, thereby reducing the overall time required to perform theregistration. Once the femur has been approximately located using acoarse registration, finer registration operations may be performed tomore accurately determine the femur location and shape. By using thefemur approximation determined by the prior registration operation asthe initial approximation of the femur in the next registrationoperation, the next registration operation may find a solution in lesstime. It is to be understood that similar considerations apply tosegmentation of other entities (and not just the femur.)

In one embodiment, each registration operation may employ a registrationframework. The registration framework may be based on three generalblocks. The first block defines a transformation model (or a class oftransforms) T(X), which may be applied to coordinates of a fixed (orreference) object (e.g., a golden femur template) to locate theircorresponding coordinates in a target image space (e.g., an MRI scan).The second block defines a metric, which quantifies the degree ofcorrespondence or similarity between features of a fixed (or reference)object and the target object (that is landmarks and appropriate targetimage features) achieved by a given transformation. It should be notedthat instead of a metric that defines the degree of correspondence, anopposite to it function is defined, which is call the defect function.The third block defines an optimization algorithm (optimizer), whichtries to maximize the reference and the target objects similarity (orminimize the opposite defect function) by changing the parameters of thetransformation model. Thus, as discussed below in detail with referenceto FIG. 37, in every registration operation 770 a-770 c and 770 e thereis a need to specify three blocks: (1) class of transforms; (2) metric(or defect) function; and (3) optimization algorithm. In one embodiment,the same third block may be used in all four registration steps. Forinstance, a gradient descent optimizer or conjugate gradient descendoptimizer may be used. Alternatively, any other appropriate optimizationalgorithm, such as Monte Carlo, simulated annealing, genetic algorithms,neural networks, and so on, may be used.

As shown in FIG. 37, which is a flowchart illustrating the process ofoperation 770 of FIG. 36, in operation 770 a translation transforms areused to register the full or entire open golden femur mesh 626 to thelandmarks 777. More specifically, in operation 770 a, the open goldenfemur mesh 626 may be approximately registered to landmarks 777 using acoarse registration transformation. In one embodiment, this may be doneby finding appropriate translation transform parameters that minimizetranslation misalignment with landmarks of the reference open goldenfemur mesh mapped onto the target femur of the target image, wherelandmarks 777 are positioned. This coarse registration operationtypically determines an approximate femur position in the MRI scan.During this operation, the reference open golden femur mesh 626 may beoverlapped with the target femur of the target image using a translationtransformation to minimize translational misalignment of the femurs. Atranslation transform, translates (or shifts) all the points in 3D spaceby the same 3D vector. That is, the reference femur may be mapped intothe target image space by shifting the reference open golden femur meshalong one or more axes in the target image space to minimizemisalignment. During this operation the reference object is not rotated,scaled or deformed. In one embodiment, three parameters for thetranslation transformation may be generated: one parameter for eachdimension that specifies the translation for that dimension. In oneembodiment, the final parameters of the translation transform minimizingthe misalignment of the mapped reference femur image coordinates intothe target image space may be found using a gradient descent optimizer.In other embodiments, other types of optimizers may be utilized, such asfor instance an Iterative Closest Point (ICP) algorithm.

Optimization of mesh alignment with respect to landmarks is based onminimizing a cost function D, which in one embodiment can be the sum,across all landmarks, of the squared distance from each landmark point777 to the transformed open golden mesh. The same cost function may beused for steps 770 a-770 c. Methods for computing this cost function andits gradient are covered in more detail later in this disclosure.

After an optimal transform has been found, it is applied to all thegolden femur data (i.e., the closed golden femur mesh 624, open goldenfemur mesh 626, and golden femur mesh regions 628, 629. The nextoperation (i.e., operation 770 b of FIG. 37, which is discussedimmediately below) is then started with transformed golden femur data.As can be understood from the following discussion, after everyconsecutive operation 770 a, 770 b, 770 c and 770 e of FIG. 37, thetransform found during the registration step is applied to all thegolden femur data. As a result, after each operation the golden femurdata is successively made more closely aligned with the femur in thetarget image.

In operation 770 b of FIG. 37 similarity transforms are used to registerthe full or entire open golden femur mesh 626 to the landmarks 777.Specifically, operation 770 b further refines the object's registrationdetermined by operation 770 a. This may be done by approximatelyregistering the open golden femur mesh 626 to landmarks 777 using asimilarity transformation. In one embodiment, a similaritytransformation may be performed in 3D space. The reference open goldenfemur mesh may be rotated in 3D, translated in 3D and homogeneouslyscaled to map its coordinates into the target MRI scan data to minimizemisalignment between the open golden femur mesh and the landmarks in thetarget MRI scan. In some embodiments, a center of rotation may bespecified so that both the rotation and scaling operations are performedwith respect to the specified center of rotation. In one embodiment, a3D similarity transformation, specified by seven parameters, may beused. One parameter specifies the scaling factor, three parametersspecify a versor that represents the 3D rotation, and three parametersspecify a vector that represents the 3D translation in each dimension. Aversor is a unit quaternion that provides a convenient mathematicalnotation for representing rotations of objects in three dimensions.

In one embodiment, local minimization techniques may be employed withthe similarity transformation to obtain a refined registration of thereference open golden femur mesh onto the target MRI scan that is notfar from the registration of the reference open golden femur mesh ontothe target MRI scan found in previous operation 770 a and used as theinitial starting approximation. For instance, gradient descent,conjugate gradient descent, or ICP optimization may be used. After thebest transform is found for operation 770 b of FIG. 37, the transform isapplied to the golden femur data in a manner similar to that ofoperation 770 a.

In operation 770 c of FIG. 37 affine transforms are used to register thefull or entire open golden femur mesh 626 to the landmarks 777.Specifically, operation 770 c further refines the image registrationdetermined by operation 770 b. In one embodiment, an affinetransformation may be used to register the open golden femur mesh 626 tolandmarks 777 in the target MRI scan data. In one embodiment, theapproximate femur registration found during operation 770 b may be usedas the initial starting approximation for the affine transformation ofoperation 770 c.

An affine transformation typically is a linear transformation followedby a translation. The affine transformation preserves collinearitybetween points (i.e., three points which lie on a line continue to becollinear after the transformation) and ratios of distances along aline. In one embodiment, a 3D affine transformation, specified by 12parameters, may be utilized. Nine parameters of the affinetransformation specify the linear transformation (which may berepresented by a three by three matrix) and three parameters of theaffine transformation specify the 3D translation in each dimension. Theparameters of the affine transform that minimizes the misalignment ofthe open golden femur mesh with landmarks may be found using again localminimization techniques, such as gradient descent or conjugate gradientdescent optimization.

After the best transform is found for operation 770 c of FIG. 37, thetransform is applied to the golden femur data. The transformed goldenfemur data from operation 770 c is then employed in the preparatory stepof detecting appropriate image edges, namely, operation 770 d, which isdiscussed below. Those edges will be later used in operation 770 e ofFIG. 37, as discussed below. The transformed golden femur data fromoperation 770 c is used as reference data similar to the previousoperations.

A discussion of image edges is now provided before discussing thedetails of operation 770 d of FIG. 37. Image edges consist of thosepoints in the image where the image contrast significantly changesbetween neighbor pixels (or voxels) and this contrast change isconsistent along several neighboring points distributed over a smoothcurve. For example, points that lie between the light cancellous bonepixels and dark cortical bone pixels form an image edge. Similarly, thepoints that lie between the dark cortical bone pixels and the grayishcartilage pixels form an image edge. Yet a configuration involving aone-pixel black spot and the surrounding light pixels does not form animage edge because the light points represent a curve with too muchcurvature, whereas the dark point represents a curve that is toodiscontinuous (spanning only a single voxel.) Usually there is an imageedge that separates one type of the body tissue from a neighboringdifferent type of body tissue.

The purpose of segmenting an image is to be able to separate in theimage certain body tissues from the surrounding tissues. Ideally, thesegmentation boundaries (or curves) should lie mostly in the imageedges. A general MRI or CT image contains lots of edges separatingvarious body tissues from the neighboring tissues. Yet when segmenting,there is only interest in certain tissues and thus particular edgesonly. Operation 770 d is intended to find those edges that are ofinterest for segmenting a particular body object.

In particular in case of the segmentation of any of the versions of thefemur planning model 22, 28 (shown in blocks 110 and 115, respectively,of FIG. 1C), operation 770 d of FIG. 37 will find the edges thatseparate the cortical femur bone from the outside knee tissues (i.e.,the tendons, ligaments, cartilage, fluid, etc.). In some embodiments,operation 770 d will not find the edges that separate the femurcancellous bone from the femur cortical bone. In other embodiments,operation 770 d will find the edges that separate the femur cancellousbone form the cortical bone.

Operation 770 d may also find some edges that are of no interest to thefemur planning segmentation. Most of those edges of no interest will lieat significant distance from the femur boundary surface and, as aresult, the edges of no interest will not influence the next operationin the algorithm, namely, operation 770 e of FIG. 37.

In some cases, some of the edges of no interest might happen to be veryclose to the edges of interest. Such nearby edges of no interest arelikely to be the edges separating the cartilage tissue from the othertissues outside the bone. Such edges might adversely influence the nextoperation in the algorithm, namely, operation 770 e of FIG. 37, and leadto inaccurate segmentation. In some embodiments, this inaccuracy can beremedied by the user providing extra landmarks 777 in the area that islikely to cause such inaccuracies or manually fixing the spline curvesduring the verification and adjustment operations.

The result of the operation 770 e of FIG. 37 will be a 3D image of thesame size as the target scan data. The resulting 3D image can be calledan edges image. The voxels in the edges image correspondent to strongedges will have highest intensities, the non-edge voxels will have lowintensities, and the voxels correspondent to weak edges will haveintermediate intensities. Discussion of the operation 770 d of FIG. 37is now provided.

In operation 770 d of FIG. 37 appropriate edges of the target images aredetected near the transformed open golden femur mesh 626. For example,as indicated in FIG. 38A, which is a flowchart illustrating the processof operation 770 d of FIG. 37, in operation 770 d 1 the signed distanceimage is computed for the transformed golden femur mesh 626. A signeddistance map is a distance map of a region in 2D (or 3D) and is afunction in 2D (or 3D). The signed distance value for a point equals thedistance from the point to the boundary of a region. A signed distancevalue can have a positive or negative value. For example, when a pointis inside the region, the signed distance value of the point is thedistance from the point to the boundary of the region in the form of anegative value. When a point is outside the region, the signed distancevalue of the point is the distance from the point to the boundary of theregion in the form of a positive value. If the signed distance mapfunction is computed in a regular grid of points in 2D (or 3D)correspondent to image pixels (or voxels) and stored as a 2D (or 3D)image representation, the result can be said to be a 2D (or 3D) signeddistance image.

Thus, from the preceding discussion, it can be understood that thesigned distance for a watertight surface is a function that has absolutevalues equal to the regular (Euclidean) distance, but the values alsohave a sign. The sign is negative for the points inside the surface, andthe sign is positive for the points outside the surface. The open goldenfemur mesh 626 transformed in operations 770 a-770 c of FIG. 37 is usedin operation 770 d 1 of FIG. 38A. By the time of operation 770 d 1, theopen golden femur mesh 626 may quite closely match the landmarks 777positioned in the target image and, as a result, the open golden femurmesh 626 also matches quite closely the target femur bone in the targetimage. Since the golden femur mesh 626 is a watertight mesh, the maskimage marking may be computed as “1” for all voxels that lie inside theopen golden femur mesh 626 and as “0” for all the voxels that lieoutside the mesh. The Signed Danielsson Distance Map Image Filter fromthe ITK library can then be used to compute the signed distance to themask boundary, which is approximately the same as the signed distance tothe mesh. It may be desired to have greater accuracy close to the mesh.If so, then for the voxels where the absolute value of the signeddistance is small, the distance to the mesh may be recomputed by findingthe closest points via a more exact method, as detailed later in thisspecification.

In operation 770 d 2 the gradient of the signed distance image iscomputed. As can be understood from FIG. 38B, the gradient of the signeddistance image contains a vector 1000 in every voxel. The vector 1000represents the gradient of the signed distance image at the particularpoint of the voxel. Because the signed distance image represents thesigned distance to the transformed open golden femur mesh 626, whichfollows closely the boundary of the femur bone in the target image, thegradient image has gradient vectors nearly orthogonal to the boundary ofthe target femur in the voxels close to the boundary.

The contour line 626 in FIG. 38B represents the approximate segmentationmesh surface found in the previous registration step of operation 770 cof FIG. 37. The vectors 1000 show the gradient of the signed distancefor the contour 626. The starting end of the vector 1000 is the point orvoxel where the vector 1000 is computed. The gradient of a signeddistance has a vector direction in every point or voxel toward theclosest point in the contour 626. Vectors 1000 are oriented from insideto outside the contour 626. Each vector 1000 has a unit length.

In operation 770 d 3 the gradient of the target image is computed. Ascan be understood from FIG. 38C, which is an enlarged view of the areain FIG. 38B enclosed by the square 1002, the gradient of the targetimage has gradient vectors 1004 orthogonal to the edges 1006, 1008 inthe target image, and the length of those vectors 1004 is larger forstronger edges and smaller for weaker edges. Such vectors 1004 arealways oriented from the darker image region to the lighter image regionor, in other words, from darker pixels towards brighter pixels. Thevectors 1004 are longer where the contrast is higher. For purposes ofillustration in FIG. 38C, the vectors 1004 illustrated are only longvectors corresponding to high contrast pixels associated with strongedges. The gradient vectors 1004 can be used to identify the outercortical bone boundary 1006 and other edges 1008, 1010 that are not ofinterest for the analysis.

Finally, operation 770 d of FIG. 37 is completed via operation 770 d 4of FIG. 38A, wherein the edges image is computed by correcting thegradient of the target image with the gradient of the signed distanceimage. As can be understood from FIG. 38D, the edges image is computedby combining the results from operations 770 d 2 and 770 d 3. Dependingon the type of 3D computer generated bone model being generated from thesegmented images, different boundary edges may be of relevance. Forexample, if the images are being segmented to generate a bone model 22,the boundary edges that are of interest contain dark cortical voxelsinside and lighter cartilage or other voxels outside. As a result, thevoxels that are of interest are those voxels that have similarlyoriented gradients 1000, 1004 computed in operations 770 d 2 and 770 d 3as shown in FIGS. 38B and 38C, respectively. In every voxel the vector1004 from operation 770 d 3 is projected onto the vector 1000 fromoperation 770 d 2. When the projection of image gradient vector onto asigned distance gradient vector points in the same direction as thesigned distance vector, its magnitude is taken as the voxel value forthe resulting edges image. When it points in the opposite direction (orhas no magnitude at all), “0” is taken as the voxel value for theresulting edges image.

The resulting edges image has high values in the target femur corticalbone outer boundary 1006. However, the edges image does not have manyother high values close to the transformed open golden femur mesh withone exception, namely, the voxels on the boundary between the targetfemur cartilage and the outsight bright voxels (for example fluidvoxels) might have high values.

As can be understood from FIG. 38D, the gradient of the signed distancevectors 1000 are uniformly oriented orthogonal to the bone surface andgo from inside to outside of the bone. The image gradient vectors 1004are oriented differently near different image details. For the points inthe bone outer boundary 1006, the vectors 1004 are almost parallel tothe vectors 1000. For two of the other boundaries 1008, the vectors1000, 1004 are generally oppositely oriented. Finally, for the lastboundary 1010, the vectors 1000, 1004 are quite differently orientedfrom each other. As a result of the combination of the vectors 1000,1004 and an analysis of their directional relationships to each other,the edges image will be for FIG. 38D as follows. For points associatedwith the bone contour line 1006, the edges image will reflect the lengthof the image gradient vector. For points associated with contour lines1008, the edges image will be zero. For points associated with thecontour line 1010, the edges image values will be smaller than thelength of the image gradient vector associated with the bone contourline 1006. Thus, the edges image will tend to have the largest valuesfor the points of the bone contour line 1006.

The high values correspondent to a cartilage/fluid boundary mightnegatively impact operation 770 e of the registration in FIG. 37.Consequently, it may be desirable to suppress those values. This can bedone in the beginning of operation 770 d 3 of FIG. 38A. Specifically, awindowing filter may be applied to the whole target image. A window [w0,w1] may be used, where w0 will be the minimum value in the image, and w1will be approximately the value correspondent to the cartilageintensity. The filter will replace the high intensity values in theimage with w1 value, and thus the boundary between the cartilage and thelighter matters will disappear. For the type of MRI images that may beused, the w1 value correspondent to the median of all the values in theimage works quite well. Although such a filter may not always suppressthe cartilage boundary entirely, it makes cartilage outer boundary verymuch weaker in the image and, as a result, the cartilage has less of animpact in operation 770 e of FIG. 37.

In one embodiment, a more sophisticated method for suppressing thecartilage boundary may be employed. The cartilage intensity values maybe estimated by comparing the voxel values near landmarks 777 along thesigned distance gradient direction. The values before a landmarkcorrespond to the cortical bone intensities, while the values after thelandmark correspond to the cartilage intensity. Thus for every landmark,a value may be found that represents an “Out of cortical bone”intensity. Such values may be interpolated into the whole image and thiswindowing function may be applied rather than the constant windowingvalue w1.

It should be appreciated that a lesser resolution than the target imageresolution may be used in all the images participating in the edgesimage computation. For example, an in-slice voxel size of 1 mm may beused rather than ˜0.3 mm in the target image. Using coarser resolutionin effect smoothes out the data, allowing a more stable edgescomputation. It also significantly speeds up the computation. In case ofvery noisy target images, an additional smoothing step may be applied.

Operation 770 of FIG. 36 is completed via operation 770 e of FIG. 37,wherein the full or entire golden femur mesh 626, including its regions628, 629, are simultaneously registered to landmarks 777 and image edgesrespectively using B-spline deformable transforms. Specifically,operation 770 e of FIG. 37 further refines the image registration of theboundary golden femur region. In one embodiment, a spline transformationmay be used to register the open golden femur mesh 626 into the MRI scandata (target image space). In one embodiment, 3D B-Spline deformabletransforms may be employed.

A B-Spline deformable transformation typically is a free formdeformation of an object using a deformation field where a deformationvector is assigned to every point in space. For example, a 3D B-splinedeformable transform T may specify a 3D vector V(P) for every point P inthe original 3D space that is moved by T such that T:P→P+V(P).

In one embodiment, a B-Spline transformation may be specified with M×Nparameters, where M is the number of nodes in the B-Spline grid and N isthe dimension of the space. In one embodiment, a 3D B-Spline deformabletransformation of order three may be used to map every reference image3D point into the target MRI scan by a different 3D vector. The field ofvectors may be modeled using B-splines. Typically a grid J×K×L ofcontrol points may be specified where J, K, and L are parameters of thetransformation.

In one embodiment, splines of order three may be used with a grid27×9×11 of control points. That is, the transformation employs 27control points in the medial/lateral direction (i.e., the x direction),9 control points in posterior/anterior direction, and 11 control pointsin distal/proximal direction. Two control points in each dimension(i.e., 2 of 27 in the x direction, 2 of 9 in the y direction and 2 of 11in the z direction) may be used to specify boundary conditions. As such,the inner spline nodes may form a grid of size 25 by 7 by 9 and theboundary conditions increase the grid to size 27 by 9 by 11. Theparametric set for this transformation has a dimension of 3×27×9×11=8019(i.e., at each node of a 27×9×11 grid of control points, there isspecified a 3-dimensional transformation vector; a nonlinearinterpolation of transformation vectors for points located betweenadjacent nodes, is governed by spline equations.) The final parametersof the spline transformation that minimizes the misalignment between thereference golden femur template and the target MRI scan data may befound.

In operation 770 e of FIG. 37 a different metric (or defect function)may be used as compared to what was used in operations 770 a, 770 b, and770 c. Specifically, a combined defect function may be used. Thecombined defect function may be defined as a linear combination of thedefect function D (same as in operations 770 a, 770 b, and 770 c) anddefect functions D_i that evaluate the discrepancy between the goldenmesh regions 628, 629 and the scan image edges defined in operation 770d of FIG. 37.

The defect function D_i, or rather its opposite metric functionM_i=−D_i, for a given Golden Mesh Region R_i may be defined as follows.All the vertices in the golden mesh region R_i, are taken, a transformis applied to them, and the correspondent intensities are evaluated inthe edges image. M_i may be set to be the sum of those intensities.Thus, when more vertices from the transformed golden mesh region R_icome close to the image edges, a higher metric value is the result.

When defining the combined metric or defect, that is when taking thelinear combination of D and all the D_i, the coefficients in the linearcombination need to be specified. It may be desirable to use a very highcoefficient with D because we want to follow very precisely thelandmarks 777 provided by a user. Smaller coefficients may be employedwith D_i. The latter coefficients might be also different. The highercoefficients may be used for those regions of the bone that require agreater degree of precision, the associated image segmentation needingto result in more clearly defined regions. The lower coefficients may beused for those regions of the bone that do not require a high degree ofprecision, the associated image segmentation resulting in less clearlydefined regions.

Some bones may have a higher degree of shape variations across thepopulation than is found with the knee region of the femur. For example,the shape of the tibia may vary more from patient to patient than doesthe shape of the femur. As a result, the affine transformation may notprovide a close enough registration of the golden tibia template to thetarget tibia in the target scan. This may cause the Splinetransformation to find a local optimum that may be far from the actualtibia in some areas. In one embodiment, an additional registrationoperation between the affine transform and spline transform operationsmay be performed to more closely align the golden tibia and the targettibia, allowing the spline transform to converge to the correct localoptimum rather than a nearby, but wrong, local optimum.

The class of transforms utilized generally should allow more flexibility(or degrees of freedom) than the Affine transform and less flexibilitythan the B-spline transforms. The number of degrees of freedom generallyis equal to the number of transform parameters. In one embodiment, aclass of transforms with more than 12 parameters and less than 3×27×9×11parameters may be used. For example, a B-spline transform with fewercontrol points than used in the subsequent spline transform may be usedfor the additional transform operation. Alternatively, the deformationsmay be modeled using quadric rather than cubic functions.

In another embodiment, several golden tibia templates may be used thatrepresent typical tibia variations, e.g., golden tibia templates forvarus, valgus, and normal tibia. In one embodiment, each of the goldentibia templates may be used during the translation, similarity andaffine transform registration operations to find the template thatprovides the best match (e.g., best correlation) in the affine transformregistration operation. This template may then be used in the remainingregistration operations.

Finally, in one embodiment, the tibia registration may be improved byperforming the tibia segmentation after the femur segmentation andadding a restriction on the tibia registration transformations such thatthe tibia may not penetrate the femur. In one embodiment, this may beimplemented by introducing a penalty for the penetration. In the targetMRI all the voxels that lie inside the femur segmentation curves may bemarked. The metric functions, described in more detail below, that areused in the registration operations may be modified to include a penaltyterm. The penalty term may be computed by taking points in the goldentibia mesh, applying a transform to the set of points, determining if atransformed sample point falls into any of the marked voxels, and addinga large value to the penalty term for each transformed sample point thatfalls into any of the marked voxels.

In each of the above registration operations, a metric may be used toquantify the degree of correspondence between the reference objects andtarget image achieved by a given transformation. In one embodiment, themetric quantitatively measures how well the transformed golden femurdata fits the target image (e.g., a target MRI scan) and landmarkspositioned there.

As discussed above, metrics M=−D, M_i=−D_i, and their linear combinationare used in operations 770 a-770 d of the registration. An explanationis now given regarding the details on how to compute those metrics. Asfar as using those metrics with optimizers that require computations ofthe gradient of the metric, it is also explained how to compute thegradient of those metrics.

When computing the metric M or rather the defect D, such a computationcan include finding the sum of the squared distances from each landmarkpoint 777 to the transformed open golden mesh. In order to make thiscomputation as quickly and efficiently as possible, the following can bedone. First, a B-Spline transformation of a mesh is no longer a mesh.The plane triangles forming the original mesh get curved over thetransformation, and the triangles are no longer planar. Rather thancomputing distances to curved triangles, which would be verycomputationally expensive, planar triangles connecting the transformedvertices are used. Very little precision is lost with this substitutionbecause the triangles are very small.

Next, after finding the transformed mesh, it is desirable for everyLandmark point to find the closest point in the transformed meshtriangles and take the squared distance to it. A spatial subdivisionscheme is used to sort all the triangles by spatial location. An octreesubdivision is used, although other schemes (kd-tree, fixed size grid,etc.) would work as well. The spatial subdivision helps to find aclosest mesh triangle and a closest point in it using an order of LOG(n)operations where n is the number of triangles in the mesh.

The optimizers used in the registration steps require the computation ofthe gradient of the metric function, which depends on the appliedtransform, over the transform parameters.

In one embodiment the metric function may be a composition of severalfunctions. For the metric M (or cost function D), for example, thefollowing functions are used in the composition: a) mesh transformation,b) distance from a Landmark point to the transformed mesh, c) squareddistance, d) sum of squares, e) inverse of the sum.

For a composition of functions, determining the gradient involvesfinding partial derivatives for each function and then applying thechain rule. The derivatives of the algebraic functions are computed bystandard formulae. The only non-trivial computation in the abovefunctions is the computation of the partial derivative of a distancefrom a point to the transformed mesh.

For the latter computation, it may involve using an approximate method.Namely, take the closest triangle found in the metric computationfollowed by taking the plane containing that triangle. This planeapproximates the transformed mesh surface in some small neighborhood ofthe closest point. One of the transform parameters is changed by a verysmall amount. It is observed where the former closest triangle is mappedafter the variation.

The plane containing the varied triangle is taken. This planeapproximates the varied transformed mesh surface in some smallneighborhood of the varied closest point. The distance from the landmarkpoint to this varied plane is taken. It is approximately the distancefrom the landmark point to the whole varied transformed mesh surface.Now the difference between the varied distance and the original distanceis taken and divided by the value of the parameters variation. Thisgives approximately the partial derivative for this parameter.

In order to compute the gradient of the metric D_i with respect to thetransform parameters, the gradient image of the edges image is computedright after the computation of the edges image itself. To compute thepartial derivative of D_i over a transform parameter, the computationmay take place for the derivative of every transformed vertex motionover that parameter using the chain rule. This derivative will be a 3Dvector. Its dot product is taken with the correspondent Gradient vectorof the Gradient Image of the Edges Image and the values are summed allover the vertices.

Finally, since the combined defect function is a linear combination ofdefect functions D and D_i, then the gradient of the combined defectfunction with respect to a given transform, is correspondingly a linearcombination (with the same coefficients) of the gradients of D and D_iwith respect to that same transform.

In summary and as can be understood from the immediately precedingdiscussion regarding operations 770 a-770 c and 770 e of FIG. 37,translation transforms (operation 770 a), similarity transforms(operation 770 b), affine transforms (operation 770 c), and B-Splinedeformable transforms (operation 770 e) are employed as part ofaccomplishing operation 770 of FIG. 36. Because in operations 770 a-770c of FIG. 37 it is intended to register the open golden femur mesh tolandmarks, the metric (or defect) function should evaluate how close thetransformed open golden femur mesh is to landmarks. Thus, in operations770 a-770 c, there is a selection of the defect function D to be the sumof squared distances from landmarks to the deformed open golden mesh. Inthe operation 770 e, a simultaneous registering of several parametersmay be defined as a combined metric that will take into account all theparameters. The combined defect function may be defined as a linearcombination of the defect function D (same as in operations 770 a-770 c)and defect functions D_i that evaluate the discrepancy between thegolden mesh regions and the scan image edges defined in operation 770 dof FIG. 37.

Once operation 770 of FIG. 36 is completed, the process of FIG. 36 thencontinues with operation 772, wherein the deformed open golden femurmesh 626 and associated regions 628, 629 are segmented followed byoperation 773, wherein the resulting segmentation curves areapproximated with splines. The process continues with operation 774,wherein the contour lines or splines generated in operation 773 aremodified to match landmarks.

In other words, in operation 774 the segmentation curves are refined tomore precisely match the landmarks. Typically, the segmentation curvescreated via the above-described algorithms match the landmarks quiteclosely. Accordingly, most any simple algorithm for a local curveadjustment can work to further refine the precision of the match of thesegmentation curves with the landmarks.

In one embodiment of operation 774 when further refining thesegmentation curves to match landmarks, only those curves that belong toslices that contain landmarks are considered. When a curve belongs to aslice with landmarks, it is assumed that it should rather precisely gothrough all the Landmarks. In one embodiment, a curve may be consideredto be precisely enough located relative to a landmark if its distancefrom the landmark (“Tol”) is Tol=0.3 mm or less. Most often all thelandmarks are within the Tol distance from the curve. However, sometimesa few of the landmarks are further than the Tol distance from the curve.As can be understood from the following discussion regarding operation774, for every curve generated via the above-described algorithms, eachlandmark in a slice is iterated. If a landmark is not within Toldistance from the curve, a correction algorithm is applied to the curveas described below with respect to operation 774.

Operation 774 locally modifies the spline curve to fit a selectedlandmark. Specifically, as can be understood from FIG. 39, which is aflowchart illustrating the process of operation 774 of FIG. 36 in thecontext of a particular image slice containing landmarks and asegmentation contour, in operation 774 a a landmark 777 is identified.In operation 774 b, the distance of the identified landmark 777 to thespline generated from the golden femur mesh 626 is computed. Morespecifically, the algorithm of operation 774 first computes distancesfor all the other landmarks in the same slice to avoid making thedistance relationships of the landmarks and curve worse.

In operation 774 c, an arc of the contour line or spline that is theclosest to the landmark is identified. Specifically, the closest splinearc [A, B] to the selected landmark L is located, where A and B areconsecutive vertices in the spline curve.

In operation 774 d, the arc is modified to include the identifiedlandmark, resulting in a modified contour line or spline. Specifically,the vertices A and B are moved iteratively so that the arc [A, B] fitsL. For each iteration, the closest point C in [A, B] to L is found. Theratio α:(1−α) is then found in which C divides [A, B]. Next, A is movedby (1−α)*(L−C), and B is moved by α*(L−C). The process stops when0.5*Tol distance is achieved.

In operation 774 e, distances of other landmarks to the modified splineare computed and reviewed in operation 774 f to verify operations 774a-774 d have not made the fit between the spline and other landmarksworse. In other words, the following is checked for every landmark.First, it is checked to see if the new distance between the spline andlandmark is within Tol and, if it is, then the relationship between thespline and landmark is acceptable. Second, it is checked to see if thenew distance between the spline and landmark is smaller than the olddistance and, if it is, then the relationship between the spline andlandmark is acceptable. Third, it is checked to see if the new distanceis higher than Tol, the old distance was higher than Tol, and the newdistance increased by less than 0.5*Tol. If the answer is yes withrespect to all three of the elements of the third check, then therelationship between the spline and landmark is acceptable. For all theother cases, the relationship between the spline and landmark is notacceptable. If the distance relationships between the spline and all ofthe landmarks are considered acceptable, the process outlined in FIG. 39is completed for the identified landmark, and the process of FIG. 39 canthen be run for another identified landmark until all landmarks havegone through the process of FIG. 39.

If any of the distance relationships between any landmark and the splineare found to be unacceptable in operation 774 f due to a modification ofthe spline with respect to a selected landmark according to operations774 a-774 d, then per operation 774 g the spline modification fromoperation 774 d is disregarded and a more local modification isemployed, as described below with respect to operations 774 h-774 k ofFIG. 39. The more local modification will add a new vertex into thespline making the spline more flexible in this region. The more localmodification will then move the new vertex to L, and this will affect avery small area of the spline. Thus, the chance of decreasing the fit toother landmarks will be very small. The more local modification occursas follows.

In operation 774 h, a point in the identified arc closest to theidentified landmark is identified. Specifically, the point C in arc [A,B] that is the closest to landmark L is found.

In operation 774 i, a spline vertex is inserted at the identified pointC. With the insertion of a new vertex, the spline shape usually changesin the two immediately adjacent neighbor arcs on both sides of the arc[A, B]. As a result, the arc spline can become too wavy in the vicinityof the arc [A, B].

To remedy the situation, the arc [A, B] is adjusted to fit the originalspline in operation 774 j. Specifically, the vertices A and B aremodified to try to fit the new spline as closely as possible to theoriginal spline. In doing so, a measure of closeness (i.e., how closelythe new spline follows the original spline in the six neighboringarcs—three to each side of the new control point C) may be computed asfollows. In one embodiment, the six spline arcs are sampled such thatthere are twenty or so sample points in every arc of the spline (i.e.,20*6 sample points). Then, the sum of the squared distances from thesample points to the original spline may be computed. Next, thecoordinates of th\e A and B vertices (control points) are varied (i.e.,two parameters for each of A and B, that is four parameters). Then, alocal optimization algorithm is used to find the closest spline. Thisprocess may be similar to the process of fitting a spline to a polyline,as described elsewhere in this Detailed Description.

Per operation 774 k, the identified point is moved to the identifiedlandmark. Specifically, the spline vertex C is moved into the landmarkpoint L.

The process outlined in FIG. 39 is completed for the identifiedlandmark, and the process of FIG. 39 can then be run for anotheridentified landmark until all landmarks have gone through the process ofFIG. 39.

Once the process of FIG. 39 is completed for all landmarks and theassociated contour lines or splines, the process of operation 774 ofFIG. 36 is considered complete, which completes the process of FIG. 36for the operation 252 a of FIG. 33. The process of operation 252 a inFIG. 33 is now complete. The image slices 16 are then scrolled over toverify if the segmentation results are acceptable, as indicated byoperation 252 c. In operation 253, if the segmentation is acceptable,then the segmentation process of FIG. 33 ends.

As can be understood from FIG. 33, if in operation 253 the segmentationis not acceptable, then the segmentation of each offending slice 16 ismodified by adding additional landmarks 777 and/or modifying thelocations of existing landmarks 777 per operation 254 of FIG. 33. Forexample and as can be understood from FIG. 40, a first spline 800, whichis generated via a first run through operation 252 of FIG. 33, hascontrol points 802 and extends along first landmarks 777 a placed in theslice 16 of FIG. 40 during operation 251 of FIG. 33.

During operation 253 of FIG. 33 the segmentation of the slice 16 of FIG.40 is identified as being unsatisfactory in the location called out byarrow A in FIG. 40. A new landmark 777 b is added in the location calledout by arrow A per operation 254 and operation 252, or morespecifically, operations 774 b-774 e of the algorithm of FIG. 39, arerepeated to generate a second spline 804, which has control points 806and extends along both the first landmarks 777 a and the second landmark777 b. As can be understood from FIG. 40, the first spline 800 and thesecond spline 804 are generally identical and coextensive, except in theregion identified by arrow A. The segmentation of the second spline 804is then approved or disapproved per operation 253. If approved, then thesegmentation process of FIG. 33 ends. If disapproved, then the secondspline 804 is further modified per operation 254 in a manner similar toas discussed above with respect to FIG. 40.

In one embodiment of operation 254 of FIG. 33, the spline may besimultaneously modified near a new added landmark or near movinglandmarks to fit the moving landmarks. In doing so, it may be the casethat the user is satisfied with the corrected splines. As a result, theprocess of FIG. 33 may simply end at operation 254 as if the entirety ofoperation 252 had been completed and the segmentation was foundacceptable at operation 253.

In one embodiment, when a user adds a new landmark into a slice with aspline, the spline is immediately modified using precisely the samealgorithm of FIG. 39, namely operations 774 b-774 e. When a user moves alandmark, the spline is updated during the motion using operations 774b-774 e of the algorithm of FIG. 39. Adding landmarks (operations 774g-774 k of the algorithm of FIG. 39) is avoided during the motion phaseas it may lead to multiple updates during motions, resulting in too manypoints.

Once the contour lines or splines are successfully segmented from eachtarget image slice, the contour lines or splines are compiled asdiscussed above into a 3D mesh that may be used as an arthritic bonemodel 36 (see FIG. 1D) or bone models 22 (see FIG. 1C).

In one embodiment of the registration process discussed above, anoptimizer may be used during the registration process to maximizesimilarity between the open golden mesh and landmarks in the targetimage (and possibly edges image) by adjusting the parameters of a giventransformation model to adjust the location of reference imagecoordinates in the target image. In one embodiment, the optimizer for aregistration operation may use the transformed golden femur data fromthe previous registration operation as its initial approximation. Then,local optimization techniques may be used to search for a local optimumnear the initial starting approximation. This may be done so that anypotential matches farther away from the feature of interest (e.g., thefemur or tibia in a knee joint) reliably found in an earlier operationmay be eliminated.

In operation 770 a of FIG. 37, when optimizing the translationtransformation, a regular step gradient descent optimizer may be used byone embodiment. Other embodiments may use different optimizationtechniques.

To find a local minimum, parameter steps may be taken in the directionof the negative of the metric gradient (or the approximate gradient)over the transform parameter space at the current point. This generallyoptimizes the metric which typically has a local minimum when featuresof the reference image mapped into corresponding features of the targetimage have minimal misalignment).

In one embodiment, initial gradient step of 3 millimeters may bespecified, a relaxation factor may be set to 0.95 and a maximum of 50iterations may be used in the regular step gradient descent optimizationmethod to determine the parameters of the translation transformationthat results in minimal misalignment between the reference Open GoldenFemur mesh and the Landmarks in the target MRI scan.

In operation 770 b of FIG. 37, when optimizing the similaritytransformation, a regular step gradient descent optimizer may be usedagain by one embodiment. When applying the regular step gradient descentoptimizer to similarity transformation, the result and the convergencerate depend on the proper choice of parameters representing thesimilarity transforms. A good choice of parameters when used withgradient computations is such that a variation of every parameter by oneunit leads to approximately equal displacement of object points. Inorder to ensure similar displacement of points with respect to threerotational parameters in the similarity transform, the initial center ofrotation for the similarity transformation may be specified as thecenter of a bounding box (or minimum sized cuboid with sides parallel tothe coordinate planes) that encloses the feature (e.g., a bone)registered in the translation registration (e.g., operation 770 a inFIG. 37). For knee segmentation, scaling coefficients of approximately40-millimeters may be used for the scaling parameters when bringing therotational angle parameters together with translation parameters. Ascaling coefficient of approximately 40-millimeters may be used becauseit is approximately half the size of the bone (in the anterior/posteriorand medial/lateral directions) of interest and results in a point beingmoved approximately 40-millimeters when performing a rotation of oneradian angle. By the same reason a scaling coefficient of 40 millimetersmay be used in the similarity transform scaling parameter together withits translational parameters.

In one embodiment, an initial gradient step of 1.5 millimeters may bespecified, a relaxation factor may be set to 0.95 and a maximum of 50iterations may be performed in the regular step gradient descentoptimization method to determine the parameters of the similaritytransformation that results in minimal misalignment between thereference open golden template mesh and landmarks in the target MRIscan.

In operation 770 c of FIG. 37, when optimizing the affinetransformation, a regular step gradient optimizer may be used again byone embodiment. For knee bones, scaling coefficients of approximately 40millimeters may be used for the matrix coefficients variations whenbringing them together with translation parameters. An initial gradientstep of 1 millimeter may be specified, the relaxation factor may be setto 0.95 and a maximum of 50 iterations may be performed to determine theparameters of the affine transformation that results in minimalmisalignment.

In operation 770 e of FIG. 37, when optimizing the B-splinetransformation, a modified regular step gradient descent optimizer maybe used by one embodiment when searching for the best B-splinedeformable transformation. Namely, a combination of regular stepgradient descent optimizer with by coordinate descent may be used here.Rather than computing one gradient vector for the transform space andtaking a step along it, a separate gradient may be computed for everyB-spline transform node. In one embodiment, order three B-splines (withJ×K×L control nodes) may be used and J×K×L gradients may be computed,one for each control point. At every iteration, each of the spline nodesmay be moved along its respective gradient. This may enable fasterconvergence of the optimization scheme. A relaxation factor of 0.95 maybe used for each spline node. A an initial gradient step ofone-millimeter may be set for every B-spline grid node, and a maximum of50 iterations may be used in the regular step gradient descentoptimization method to find the parameters of the B-splinetransformation that provides minimal misalignment of the open goldenfemur mesh and landmarks and feature edges in the target MRI scan.

FIG. 23 depicts a flowchart illustrating one method for generatingspline curves outlining the surface of an object of interest in eachtarget MRI slice (e.g., as discussed above with respect to operation 772of FIG. 36) after the transformed golden femur mesh is found inoperation 770 e in FIG. 37. Initially, operation 470 intersects thetransformed golden femur mesh with a slice of the target scan data. Theintersection defines a polyline curve of the surface of the feature(e.g., bone) in each slice. Two or more polyline curves may be generatedin a slice when the bone is not very straightly positioned with respectto the slice direction.

A polyline curve is a piecewise linear approximation to a curved featureshape. Generally, this curve should be easy to manipulate with a set ofcontrol points. The polyline curve may have many segments, making itmore difficult to manipulate the polyline curve (e.g., during operation254 or 260 of FIG. 6). One embodiment may generate one or more Kochaneksplines from the polyline curve. Each spline typically has a smallernumber of control points and typically fits the polyline curve withabout 0.3-millimeter deviation. See previous description in thisDetailed Description for a detailed discussion regarding splinegeneration.

As discussed above, in one embodiment, the output of the segmentationmay be a triangular mesh (e.g., a 3D surface model) of the segmentedbone(s) of a joint (e.g., the femur and tibia of a knee joint). The meshgenerated generally represents a watertight surface that closely followsthe segmentation contour curves of the slices, smoothly interpolatesbetween the segmentation contour curves, and may have a low triangularcount. See previous description in this Detailed Description for adetailed discussion regarding mesh generation and the manual adjustmentof segmentation splines.

The 3D surface models of the lower end of the femur and the upper end ofthe tibia of a patient's knee may be used to create arthroplasty jigsand/or implants. For example, the models may be used to create femur andtibia jigs that can be used with a patient's femur and tibia asdisclosed in the various U.S. Patent Applications incorporated byreference herein in this Detailed Description and filed by Park and Parket al. The automatic or semi-automatic processes described herein forsegmentation of image data to generate 3D bone models may reduce theoverall time required to perform a reconstructive surgery to repair adysfunctional joint and may also provide improved patient outcomes.

III. Overview of Overestimation Process

The description in Section II. focused on the acquisition of medicalimages, the segmentation or auto-segmentation of the medical images, andthe generation of a patient bone model from the segmented images that isrepresentative of the bones of the patient in a deteriorated ordegenerated state. Beginning in Section III., the present disclosuredescribes an overestimation process where certain areas of the bone inthe medical images are identified for generating mating jig surfaces,and certain areas of the bone in the medical images are identified asnon-mating areas between a jig and the bone surface. Subsequently,Section IV. will describe an overview of the pre-operative surgicalplanning process that may take place on the patient's image data.

a. Overview of System and Method for Manufacturing CustomizedArthroplasty Cutting Jigs

This section continues and expands upon the previous description of theoverview of systems and methods for manufacturing custom arthroplastyjigs of FIGS. 1A-1E. Referring back to FIG. 1D, to coordinate thepositions/orientations of the bone and arthritic models 36, 36 and theirrespective descendants, any movement of the restored bone models 28 frompoint P to point P′ is tracked to cause a generally identicaldisplacement for the “arthritic models” 36 [block 135].

As depicted in FIG. 1D, computer generated 3D surface models 40 of thearthroplasty target areas 42 of the arthritic models 36 are importedinto computer generated 3D arthroplasty jig models 38 [block 140]. Thus,the jig models 38 are configured or indexed to matingly receive thearthroplasty target areas 42 of the arthritic models 36. Jigs 2manufactured to match such jig models 38 will then matingly receive thearthroplasty target areas of the actual joint bones during thearthroplasty surgical procedure.

In some embodiments, the 3D surface models 40 may be modified to accountfor irregularities in the patient's bone anatomy or limitations in theimaging process. For example, the 3D surface models 40 may be subjectedto, or the result of, an “overestimation” process. The “overestimated”3D surface models 40 may result in bone mating surfaces of the actualjigs that matingly receive and contact certain portions of thearthroplasty target areas of the actual joint bones while other portionsof the jigs are spaced apart from the bones, including, for example,some regions of the arthroplasty target areas of the actual joint bones.Thus, the bone mating surfaces of the actual jigs may matingly contactcertain specific portions of the arthroplasty target areas of the actualjoint bones while other areas of the arthroplasty target areas are notmatingly contacted. In some embodiments, the specific portions of thearthroplasty target areas contacted by the jig's bone mating surfacesmay be those areas that are most likely to be accurately 3D computermodeled and most likely to result in a reliably accurate mating contactbetween the jig's bone mating surface and the arthroplasty target areas,and the portions of the arthroplasty target areas not contacted by thejig's bone mating surfaces may be those areas that are the least likelyto be accurately 3D computer modeled.

In other words, for some embodiments, overestimation may result in areasof mating contact for the bone mating surfaces of the actual jigs beingbased on the areas of the 3D surface models that are most reliablyaccurate with respect to the image scan data and most readily machinedvia the tooling of the CNC machine. Conversely, for some embodiments,overestimation may result in areas of non-contact for the bone mating orother surfaces of the actual jigs for those areas of the jig pertainingto those areas of the 3D surface models that result from image scan datathat is less accurate or reliable and/or represent bone features thatare too small to be readily machined via the tooling of the CNC machine.The result of the overestimation process described below is actual jigswith a bone mating surfaces that matingly contact certain reliableregions of the arthroplasty target areas of the actual joint bones whileavoiding contact with certain less reliable regions of the arthroplastytarget areas, resulting in jigs with bone mating surfaces thataccurately and reliably matingly receive the arthroplasty targetregions.

In one embodiment, the procedure for indexing the jig models 38 to thearthroplasty target areas 42 is a manual process. The 3D computergenerated models 36, 38 are manually manipulated relative to each otherby a person sitting in front of a computer 6 and visually observing thejig models 38 and arthritic models 36 on the computer screen 9 andmanipulating the models 36, 38 by interacting with the computer controls11. In one embodiment, by superimposing the jig models 38 (e.g., femurand tibia arthroplasty jigs in the context of the joint being a knee)over the arthroplasty target areas 42 of the arthritic models 36, orvice versa, the surface models 40 of the arthroplasty target areas 42can be imported into the jig models 38, resulting in jig models 38indexed to matingly receive the arthroplasty target areas 42 of thearthritic models 36. Point P′ (X_(0-k), Y_(0-k), Z_(0-k)) can also beimported into the jig models 38, resulting in jig models 38 positionedand oriented relative to point P′ (X_(0-k), Y_(0-k), Z_(0-k)) to allowtheir integration with the bone cut and drill hole data 44 of [block125].

In one embodiment, the procedure for indexing the jig models 38 to thearthroplasty target areas 42 is generally or completely automated, asdiscussed in detail later in this Detailed Description. For example, acomputer program may create 3D computer generated surface models 40 ofthe arthroplasty target areas 42 of the arthritic models 36. Thecomputer program may then import the surface models 40 and point P′(X_(0-k), Y_(0-k), Z_(0-k)) into the jig models 38, resulting in the jigmodels 38 being indexed to matingly receive the arthroplasty targetareas 42 of the arthritic models 36. In some embodiments, the surfacemodels 40 may include accounting for irregularities in the patient'sbone anatomy and/or limitations in the imaging technology by creatingdeliberate gaps between the jig's surface and the patient's bone. Theresulting jig models 38 are also positioned and oriented relative topoint P′ (X_(0-k), Y_(0-k), Z_(0-k)) to allow their integration with thebone cut and drill hole data 44 of [block 125].

In one embodiment, the arthritic models 36 may be 3D volumetric modelsas generated from the closed-loop process discussed below with respectto FIGS. 41D-41F. In other embodiments, the arthritic models 36 may be3D surface models as generated from the open-loop process discussedbelow with respect to FIGS. 41A-41C and 43A-43C.

As indicated in FIG. 1E, in one embodiment, the data regarding the jigmodels 38 and surface models 40 relative to point P′ (X_(0-k), Y_(0-k),Z_(0-k)) is packaged or consolidated as the “jig data” 46 [block 145].The “jig data” 46 is then used as discussed below with respect to [block150] in FIG. 1E.

As can be understood from FIG. 1E, the “saw cut and drill hole data” 44is integrated with the “jig data” 46 to result in the “integrated jigdata” 48 [block 150]. As explained above, since the “saw cut and drillhole data” 44, “jig data” 46 and their various ancestors (e.g., models22, 28, 36, 38) are matched to each other for position and orientationrelative to point P and P′, the “saw cut and drill hole data” 44 isproperly positioned and oriented relative to the “jig data” 46 forproper integration into the “jig data” 46. The resulting “integrated jigdata” 48, when provided to the CNC machine 10, results in jigs 2: (1)configured to matingly receive the arthroplasty target areas of thepatient's bones; and (2) having cut slots and drill holes thatfacilitate preparing the arthroplasty target areas in a manner thatallows the arthroplasty joint implants to generally restore thepatient's joint line to its pre-degenerated or natural alignment state.

As can be understood from FIGS. 1A and 1E, the “integrated jig data” 48is transferred from the computer 6 to the CNC machine 10 [block 155].Jig blanks 50 are provided to the CNC machine 10 [block 160], and theCNC machine 10 employs the “integrated jig data” to machine thearthroplasty jigs 2 from the jig blanks 50.

For a discussion of example customized arthroplasty cutting jigs 2capable of being manufactured via the above-discussed process, referenceis made to FIGS. 1F-1I. While, as pointed out above, the above-discussedprocess may be employed to manufacture jigs 2 configured forarthroplasty procedures involving knees, elbows, ankles, wrists, hips,shoulders, vertebra interfaces, etc., the jig examples depicted in FIGS.1F-1I are for total knee replacement (“TKR”) procedures. Thus, FIGS. 1Fand 1G are, respectively, bottom and top perspective views of an examplecustomized arthroplasty femur jig 2A, and FIGS. 1H and 1I are,respectively, bottom and top perspective views of an example customizedarthroplasty tibia jig 2B.

As indicated in FIGS. 1F and 1G, a femur arthroplasty jig 2A may includean interior side or portion 100 and an exterior side or portion 102.When the femur cutting jig 2A is used in a TKR procedure, the interiorside or portion 100 faces and matingly receives the arthroplasty targetarea 42 of the femur lower end, and the exterior side or portion 102 ison the opposite side of the femur cutting jig 2A from the interiorportion 100.

The interior portion 100 of the femur jig 2A is configured to match thesurface features of the damaged lower end (i.e., the arthroplasty targetarea 42) of the patient's femur 18. Thus, when the target area 42 isreceived in the interior portion 100 of the femur jig 2A during the TKRsurgery, the surfaces of the target area 42 and the interior portion 100match.

The surface of the interior portion 100 of the femur cutting jig 2A ismachined or otherwise formed into a selected femur jig blank 50A and isbased or defined off of a 3D surface model 40 of a target area 42 of thedamaged lower end or target area 42 of the patient's femur 18. In someembodiments, the 3D surface model 40 may modified via the“overestimation” process described below to account for limitations inthe medical imaging process and/or limitations in the machining process.

As indicated in FIGS. 1H and 1I, a tibia arthroplasty jig 2B may includean interior side or portion 104 and an exterior side or portion 106.When the tibia cutting jig 2B is used in a TKR procedure, the interiorside or portion 104 faces and matingly receives the arthroplasty targetarea 42 of the tibia upper end, and the exterior side or portion 106 ison the opposite side of the tibia cutting jig 2B from the interiorportion 104.

The interior portion 104 of the tibia jig 2B is configured to match thesurface features of the damaged upper end (i.e., the arthroplasty targetarea 42) of the patient's tibia 20. Thus, when the target area 42 isreceived in the interior portion 104 of the tibia jig 2B during the TKRsurgery, the surfaces of the target area 42 and the interior portion 104match.

The surface of the interior portion 104 of the tibia cutting jig 2B ismachined or otherwise formed into a selected tibia jig blank 50B and isbased or defined off of a 3D surface model 40 of a target area 42 of thedamaged upper end or target area 42 of the patient's tibia 20. In someembodiments, the 3D surface model 40 may modified via the“overestimation” process described below to account for limitations inthe medical imaging process and/or limitations in the machining process.

b. Overview of Automated Process for Indexing 3D Arthroplasty Jig Modelsto Arthroplasty Target Areas

As mentioned above with respect to [block 140] of FIG. 1D, the processfor indexing the 3D arthroplasty jig models 38 to the arthroplastytarget areas 42 can be automated. A discussion of an example of such anautomated process will now concern the remainder of this DetailedDescription, beginning with an overview of the automated indexingprocess.

As can be understood from FIG. 1A and [blocks 100-105] of FIG. 1B, apatient 12 has a joint 14 (e.g., a knee, elbow, ankle, wrist, shoulder,hip, vertebra interface, etc.) to be replaced. The patient 12 has thejoint 14 scanned in an imaging machine 8 (e.g., a CT, MRI, etc. machine)to create a plurality of 2D scan images 16 of the bones (e.g., femur 18and tibia 20) forming the patient's joint 14 (e.g., knee). Each scanimage 16 is a thin slice image of the targeted bone(s) 18, 20. The scanimages 16 are sent to the CPU 7, which employs an open-loop imageanalysis along targeted features 42 of the scan images 16 of the bones18, 20 to generate a contour line for each scan image 16 along theprofile of the targeted features 42.

As can be understood from FIG. 1A and [block 110] of FIG. 1C, the CPU 7compiles the scan images 16 and, more specifically, the contour lines togenerate 3D computer surface models (“arthritic models”) 36 of thetargeted features 42 of the patient's joint bones 18, 20. In the contextof total knee replacement (“TKR”) surgery, the targeted features 42 maybe the lower or knee joint end of the patient's femur 18 and the upperor knee joint end of the patient's tibia 20. More specifically, thetargeted features 42 may be the tibia contacting articulating surface ofthe patient's femur 18 and the femur contacting articulating surface ofthe patient's tibia 20.

In some embodiments, the “arthritic models” 36 may be surface models orvolumetric solid models respectively formed via an open-loop orclosed-loop process such that the contour lines are respectively open orclosed loops. In one embodiment discussed in detail herein, the“arthritic models” 36 may be surface models formed via an open-loopprocess. By employing an open-loop and surface model approach, asopposed to a closed-loop and volumetric solid model approach, thecomputer modeling process requires less processing capability and timefrom the CPU 7 and, as a result, is more cost effective.

The system 4 measures the anterior-posterior extent and medial-lateralextent of the target areas 42 of the “arthritic models” 36. Theanterior-posterior extent and medial-lateral extent may be used todetermine an aspect ratio, size and/or configuration for the 3D“arthritic models” 36 of the respective bones 18, 20. In one embodimentof a jig blank grouping and selection method discussed below, the aspectratio, size and/or configuration of the 3D “arthritic models” 36 of therespective bones 18, 20 may be used for comparison to the aspect ratio,size and/or configuration of 3D computer models of candidate jig blanks50 in a jig blank grouping and selection method discussed below. In oneembodiment of a jig blank grouping and selection method discussed below,the anterior-posterior and medial-lateral dimensions of the 3D“arthritic models” 36 of the respective bones 18, 20 may be used forcomparison to the anterior-posterior and medial-lateral dimensions of 3Dcomputer models of candidate jig blanks 50.

In the context of TKR, the jigs 2 will be femur and tibia arthroplastycutting jigs 2A, 2B, which are machined or otherwise formed from femurand tibia jig blanks 50A, 50B. A plurality of candidate jig blank sizesexists, for example, in a jig blank library. While each candidate jigblank may have a unique combination of anterior-posterior andmedial-lateral dimension sizes, in some embodiments, two or more of thecandidate jig blanks may share a common aspect ratio or configuration.The candidate jig blanks of the library may be grouped along slopedlines of a plot according to their aspect ratios. The system 4 employsthe jig blank grouping and selection method to select a jig blank 50from a plurality of available jig blank sizes contained in the jig blanklibrary. For example, the configurations, sizes and/or aspect ratios ofthe tibia and femur 3D arthritic models 36 are compared to theconfigurations, sizes and/or aspect ratios of the 3D models of thecandidate jig blanks with or without a dimensional comparison betweenthe arthritic models 36 and the models of the candidate jig blanks.

Alternatively, in one embodiment, the anterior-posterior andmedial-lateral dimensions of the target areas of the arthritic models 36of the patient's femur and tibia 18, 20 are increased via a mathematicalformula. The resulting mathematically modified anterior-posterior andmedial-lateral dimensions are then compared to the anterior-posteriorand medial-lateral dimensions of the models of the candidate jig blanks50A, 50B. In one embodiment, the jig blanks 50A, 50B selected are thejig blanks having anterior-posterior and medial-lateral dimensions thatare the closest in size to the mathematically modifiedanterior-posterior and medial-lateral dimensions of the patient's bones18, 20 without being exceeded by the mathematically modified dimensionsof the patient's bones 18, 20. In one embodiment, the jig blankselection method results in the selection of a jig blank 50 that is asnear as possible in size to the patient's knee features, therebyminimizing the machining involved in creating a jig 2 from a jig blank.

In one embodiment, as discussed with respect to FIGS. 1F-1I, eacharthroplasty cutting jig 2 includes an interior portion and an exteriorportion. The interior portion is dimensioned specific to the surfacefeatures of the patient's bone that are the focus of the arthroplasty.Thus, where the arthroplasty is for TKR surgery, the jigs will be afemur jig and/or a tibia jig. The femur jig will have an interiorportion custom configured to match the damaged surface of the lower orjoint end of the patient's femur. The tibia jig will have an interiorportion custom configured to match the damaged surface of the upper orjoint end of the patient's tibia.

In one embodiment, because of the jig blank grouping and selectionmethod, the exterior portion of each arthroplasty cutting jig 2 issubstantially similar in size to the patient's femur and tibia 3Darthritic models 36. However, to provide adequate structural integrityfor the cutting jigs 2, the exterior portions of the jigs 2 may bemathematically modified to cause the exterior portions of the jigs 2 toexceed the 3D femur and tibia models in various directions, therebyproviding the resulting cutting jigs 2 with sufficient jig materialbetween the exterior and interior portions of the jigs 2 to provideadequate structural strength.

As can be understood from [block 140] of FIG. 1D, once the system 4selects femur and tibia jig blanks 50 of sizes and configurationssufficiently similar to the sizes and configurations of the patient'sfemur and tibia computer arthritic models 36, the system 4 superimposesthe 3D computer surface models 40 of the targeted features 42 of thefemur 18 and tibia 20 onto the interior portion of the respective 3Dcomputer models of the selected femur and tibia jigs 38, or moreappropriately in one version of the present embodiment, the jig blanks50. The result, as can be understood from [block 145] of FIG. 1E, iscomputer models of the femur and tibia jigs 2 in the form of “jig data”46, wherein the femur and tibia jig computer models have: (1) respectiveexterior portions closely approximating the overall size andconfiguration of the patient's femur and tibia; and (2) respectiveinterior portions having surfaces that match the targeted features 42 ofthe patient's femur 18 and tibia 20.

The system 4 employs the data from the jig computer models (i.e., “jigdata” 46) to cause the CNC machine 10 to machine the actual jigs 2 fromactual jig blanks. The result is the automated production of actualfemur and tibia jigs 2 having: (1) exterior portions generally matchingthe patient's actual femur and tibia with respect to size and overallconfiguration; and (2) interior portions having patient-specificdimensions and configurations corresponding to the actual dimensions andconfigurations of the targeted features 42 of the patient's femur andtibia. The systems 4 and methods disclosed herein allow for theefficient manufacture of arthroplasty jigs 2 customized for the specificbone features of a patient.

The jigs 2 and systems 4 and methods of producing such jigs areillustrated herein in the context of knees and TKR surgery. However,those skilled in the art will readily understand the jigs 2 and system 4and methods of producing such jigs can be readily adapted for use in thecontext of other joints and joint replacement surgeries, e.g., elbows,shoulders, hips, etc. Accordingly, the disclosure contained hereinregarding the jigs 2 and systems 4 and methods of producing such jigsshould not be considered as being limited to knees and TKR surgery, butshould be considered as encompassing all types of joint surgeries.

c. Defining a 3D Surface Model of an Arthroplasty Target Area of a FemurLower End for Use as a Surface of an Interior Portion of a FemurArthroplasty Cutting Jig.

For a discussion of a method of generating a 3D model 40 of a targetarea 42 of a damaged lower end 204 y of a patient's femur 18, referenceis made to FIGS. 41A-41G. FIG. 41A is an anterior-posterior (“AP”) imageslice 208 y of the damaged lower or knee joint end 204 y of thepatient's femur 18, wherein the image slice 208 y includes an open-loopcontour line segment 210 y corresponding to the target area 42 of thedamaged lower end 204 y. FIG. 41B is a plurality of image slices (16-1,16-1, 16-2, . . . 16-n) with their respective open-loop contour linesegments (210 y-1, 210 y-2, . . . 210 y-n), the open-loop contour linesegments 210 y being accumulated to generate the 3D model 40 of thetarget area 42. FIG. 41C is a 3D model 40 of the target area 42 of thedamaged lower end 204 y as generated using the open-loop contour linesegments (16-1, 16-2, . . . 16-n) depicted in FIG. 41B. FIGS. 41D-41Fare respectively similar to FIGS. 41A-41C, except FIGS. 41D-41 F pertainto a closed-loop contour line as opposed to an open-loop contour line.FIG. 41G is a flow chart illustrating an overview of the method ofproducing a femur jig 2A.

As can be understood from FIGS. 1A, 1B and 41A, the imager 8 is used togenerate a 2D image slice 16 of the damaged lower or knee joint end 204y of the patient's femur 18. As depicted in FIG. 41A, the 2D image 16may be an AP view of the femur 18. Depending on whether the imager 8 isa MRI or CT imager, the image slice 16 will be a MRI or CT slice. Thedamaged lower end 204 y includes the posterior condyle 212 y, ananterior femur shaft surface 214 y, and an area of interest or targetedarea 42 that extends from the posterior condyle 212 y to the anteriorfemur shaft surface 214 y. The targeted area 42 of the femur lower endmay be the articulating contact surfaces of the femur lower end thatcontact corresponding articulating contact surfaces of the tibia upperor knee joint end.

As shown in FIG. 41A, the image slice 16 may depict the cancellous bone216 y, the cortical bone 218 y surrounding the cancellous bone, and thearticular cartilage lining portions of the cortical bone 218 y. Thecontour line 210 y may extend along the targeted area 42 and immediatelyadjacent the cortical bone and cartilage to outline the contour of thetargeted area 42 of the femur lower end 204 y. The contour line 210 yextends along the targeted area 42 starting at point A on the posteriorcondyle 212 y and ending at point B on the anterior femur shaft surface214 y.

In one embodiment, as indicated in FIG. 41A, the contour line 210 yextends along the targeted area 42, but not along the rest of thesurface of the femur lower end 204 y. As a result, the contour line 210y forms an open-loop that, as will be discussed with respect to FIGS.41B and 41C, can be used to form an open-loop region or 3D computermodel 40, which is discussed with respect to [block 140] of FIG. 1D andclosely matches the 3D surface of the targeted area 42 of the femurlower end. Thus, in one embodiment, the contour line is an open-loop anddoes not outline the entire cortical bone surface of the femur lower end204 y. Also, in one embodiment, the open-loop process is used to formfrom the 3D images 16 a 3D surface model 36 that generally takes theplace of the arthritic model 36 discussed with respect to [blocks125-140] of FIG. 1D and which is used to create the surface model 40used in the creation of the “jig data” 46 discussed with respect to[blocks 145-150] of FIG. 1E.

In one embodiment and in contrast to the open-loop contour line 210 ydepicted in FIGS. 41A and 41B, the contour line is a closed-loop contourline 210 y′ that outlines the entire cortical bone surface of the femurlower end and results in a closed-loop area, as depicted in FIG. 41D.The closed-loop contour lines 210 y′-2, . . . 210 y′-n of each imageslice 16-1, . . . 16-n are combined, as indicated in FIG. 41E. Aclosed-loop area may require the analysis of the entire surface regionof the femur lower end 204 y and result in the formation of a 3D modelof the entire femur lower end 204 y as illustrated in FIG. 41F. Thus,the 3D surface model resulting from the closed-loop process ends uphaving in common much, if not all, the surface of the 3D arthritic model36. In one embodiment, the closed-loop process may result in a 3Dvolumetric anatomical joint solid model from the 2D images 16 viaapplying mathematical algorithms. U.S. Pat. No. 5,682,886, which wasfiled Dec. 26, 1995 and is incorporated by reference in its entiretyherein, applies a snake algorithm forming a continuous boundary orclosed-loop. After the femur has been outlined, a modeling process isused to create the 3D surface model, for example, through a Bezierpatches method. Other 3D modeling processes, e.g.,commercially-available 3D construction software as listed in other partsof this Detailed Description, are applicable to 3D surface modelgeneration for closed-loop, volumetric solid modeling.

In one embodiment, the closed-loop process is used to form from the 3Dimages 16 a 3D volumetric solid model 36 that is essentially the same asthe arthritic model 36 discussed with respect to [blocks 125-140] ofFIG. 1D. The 3D volumetric solid model 36 is used to create the surfacemodel 40 used in the creation of the “jig data” 46 discussed withrespect to [blocks 145-150] of FIG. 1E.

The formation of a 3D volumetric solid model of the entire femur lowerend employs a process that may be much more memory and time intensivethan using an open-loop contour line to create a 3D model of thetargeted area 42 of the femur lower end. Accordingly, although theclosed-loop methodology may be utilized for the systems and methodsdisclosed herein, for at least some embodiments, the open-loopmethodology may be preferred over the closed-loop methodology.

An example of a closed-loop methodology is disclosed in U.S. patentapplication Ser. No. 11/641,569 to Park, which is entitled “ImprovedTotal Joint Arthroplasty System” and was filed Jan. 19, 2007. Thisapplication is incorporated by reference in its entirety into thisDetailed Description.

As can be understood from FIGS. 41B and 41G, the imager 8 generates aplurality of image slices (16-1, 16-2 . . . 16-n) via repetitive imagingoperations [block 1000]. Each image slice 16 has an open-loop contourline (210 y-1, 210 y-2 . . . 210 y-n) extending along the targetedregion 42 in a manner as discussed with respect to FIG. 41A [block1005]. In one embodiment, each image slice is a two-millimeter 2D imageslice 16. The system 4 compiles the plurality of 2D image slices (16-1,16-2 . . . 16-n) and, more specifically, the plurality of open-loopcontour lines (210 y-1, 210 y-2, . . . 210 y-n) into the 3D femursurface computer model 40 depicted in FIG. 41C [block 1010]. Thisprocess regarding the generation of the surface model 40 is alsodiscussed in the overview section with respect to [blocks 100-105] ofFIG. 1B and [blocks 130-140] of FIG. 1D. A similar process may beemployed with respect to the closed-loop contour lines depicted in FIGS.41D-41F.

As can be understood from FIG. 41C, the 3D femur surface computer model40 is a 3D computer representation of the targeted region 42 of thefemur lower end. In one embodiment, the 3D representation of thetargeted region 42 is a 3D representation of the articulated tibiacontact surfaces of the femur distal end. As the open-loop generated 3Dmodel 40 is a surface model of the relevant tibia contacting portions ofthe femur lower end, as opposed to a 3D model of the entire surface ofthe femur lower end as would be a result of a closed-loop contour line,the open-loop generated 3D model 40 is less time and memory intensive togenerate.

In one embodiment, the open-loop generated 3D model 40 is a surfacemodel of the tibia facing end face of the femur lower end, as opposed a3D model of the entire surface of the femur lower end. The 3D model 40can be used to identify the area of interest or targeted region 42,which, as previously stated, may be the relevant tibia contactingportions of the femur lower end. Again, the open-loop generated 3D model40 is less time and memory intensive to generate as compared to a 3Dmodel of the entire surface of the femur distal end, as would begenerated by a closed-loop contour line. Thus, for at least someversions of the embodiments disclosed herein, the open-loop contour linemethodology is preferred over the closed-loop contour line methodology.However, the system 4 and method disclosed herein may employ either theopen-loop or closed-loop methodology and should not be limited to one orthe other.

Regardless of whether the 3D model 40 is a surface model of the targetedregion 42 (i.e., a 3D surface model generated from an open-loop processand acting as the arthritic model 22) or the entire tibia facing endface of the femur lower end (i.e., a 3D volumetric solid model generatedfrom a closed-loop process and acting as the arthritic model 22), thedata pertaining to the contour lines 210 y can be converted into the 3Dcontour computer model 40 via the surface rendering techniques disclosedin any of the aforementioned U.S. patent applications to Park. Forexample, surface rending techniques employed include point-to-pointmapping, surface normal vector mapping, local surface mapping, andglobal surface mapping techniques. Depending on the situation, one or acombination of mapping techniques can be employed.

In one embodiment, the generation of the 3D model 40 depicted in FIG.41C may be formed by using the image slices 16 to determine locationcoordinate values of each of a sequence of spaced apart surface pointsin the open-loop region of FIG. 41B. A mathematical model may then beused to estimate or compute the 3D model 40 in FIG. 41C. Examples ofother medical imaging computer programs that may be used include, butare not limited to: Analyze from AnalyzeDirect, Inc. of Overland Park,Kans.; open-source software such as Paraview of Kitware, Inc.; InsightToolkit (“ITK”) available at www.itk.org; 3D Slicer available atwww.slicer.org; and Mimics from Materialise of Ann Arbor, Mich.

Alternatively or additionally to the aforementioned systems forgenerating the 3D model 40 depicted in FIG. 41C, other systems forgenerating the 3D model 40 of FIG. 41C include the surface renderingtechniques of the Non-Uniform Rational B-spline (“NURB”) program or theBézier program. Each of these programs may be employed to generate the3D contour model 40 from the plurality of contour lines 210 y.

In one embodiment, the NURB surface modeling technique is applied to theplurality of image slices 16 and, more specifically, the plurality ofopen-loop contour lines 210 y of FIG. 41B. The NURB software generates a3D model 40 as depicted in FIG. 41C, wherein the 3D model 40 has areasof interest or targeted regions 42 that contain both a mesh and itscontrol points. For example, see Ervin et al., Landscape Modeling,McGraw-Hill, 2001, which is hereby incorporated by reference in itsentirety into this Detailed Description.

In one embodiment, the NURB surface modeling technique employs thefollowing surface equation:

${{G\left( {s,t} \right)} = \frac{\sum\limits_{i = 0}^{k\; 1}{\sum\limits_{j = 0}^{k\; 2}{{W\left( {i,j} \right)}{P\left( {i,j} \right)}{b_{i}(s)}{b_{j}(t)}}}}{\sum\limits_{i = 0}^{k\; 1}{\sum\limits_{j = 0}^{k\; 2}{{W\left( {i,j} \right)}{b_{i}(s)}{b_{j}(t)}}}}},$wherein P(i,j) represents a matrix of vertices with nrows=(k1+1) andncols=(k2+1), W(i,j) represents a matrix of vertex weights of one pervertex point, b_(i)(s) represents a row-direction basis or blending ofpolynomial functions of degree M1, b_(j)(t) represents acolumn-direction basis or blending polynomial functions of degree M2, srepresents a parameter array of row-direction knots, and t represents aparameter array of column-direction knots.

In one embodiment, the Bézier surface modeling technique employs theBézier equation (1972, by Pierre Bézier) to generate a 3D model 40 asdepicted in FIG. 41C, wherein the model 40 has areas of interest ortargeted regions 42. A given Bézier surface of order (n, m) is definedby a set of (n+1)(m+1) control points k_(i,j). It maps the unit squareinto a smooth-continuous surface embedded within a space of the samedimensionality as (k_(i,j)). For example, if k are all points in afour-dimensional space, then the surface will be within afour-dimensional space. This relationship holds true for aone-dimensional space, a two-dimensional space, a fifty-dimensionalspace, etc.

A two-dimensional Bézier surface can be defined as a parametric surfacewhere the position of a point p as a function of the parametriccoordinates u, v is given by:

${p\left( {u,v} \right)} = {\sum\limits_{i = 0}^{n}{\sum\limits_{j = 0}^{m}{{B_{i}^{n}(u)}{B_{j}^{m}(v)}k_{i,j}}}}$evaluated over the unit square, where

${B_{i}^{n}(u)} = {\begin{pmatrix}n \\i\end{pmatrix}{u^{i}\left( {i - u} \right)}^{n - i}}$is a Bernstein polynomial and

$\begin{pmatrix}n \\i\end{pmatrix} = \frac{n!}{{i!}*{\left( {n - i} \right)!}}$is the binomial coefficient. See Grune et al, On Numerical Algorithm andInteractive Visualization for Optimal Control Problems, Journal ofComputation and Visualization in Science, Vol. 1, No. 4, July 1999,which is hereby incorporated by reference in its entirety into thisDetailed Description.

Various other surface rendering techniques are disclosed in otherreferences. For example, see the surface rendering techniques disclosedin the following publications: Lorensen et al., Marching Cubes: A highResolution 3d Surface Construction Algorithm, Computer Graphics, 21-3:163-169, 1987; Farin et al., NURB Curves &Surfaces: From ProjectiveGeometry to Practical Use, Wellesley, 1995; Kumar et al, RobustIncremental Polygon Triangulation for Surface Rendering, WSCG, 2000;Fleischer et al., Accurate Polygon Scan Conversion Using Half-OpenIntervals, Graphics Gems III, p. 362-365, code: p. 599-605, 1992; Foleyet al., Computer Graphics: Principles and Practice, Addison Wesley,1990; Glassner, Principles of Digital Image Synthesis, Morgan Kaufmann,1995, all of which are hereby incorporated by reference in theirentireties into this Detailed Description.

d. Selecting a Jig Blank Most Similar in Size and/or Configuration tothe Size of the Patient's Femur Lower End.

As mentioned above, an arthroplasty jig 2, such as a femoral jig 2Aincludes an interior portion 100 and an exterior portion 102. Thefemoral jig 2A is formed from a femur jig blank 50A, which, in oneembodiment, is selected from a finite number of femur jig blank sizes.The selection of the femur jig blank 50A is based on a comparison of thedimensions of the patient's femur lower end 204 y to the dimensionsand/or configurations of the various sizes of femur jig blanks 50A toselect the femur jig blank 50A most closely resembling the patient'sfemur lower end 204 y with respect to size and/or configuration. Thisselected femur jig blank 50A has an outer or exterior side or surface232 y that forms the exterior portion 232 y of the femur jig 2A. The 3Dsurface computer model 40 discussed with respect to the immediatelypreceding section of this Detail Description is used to define a 3Dsurface 40 into the interior side 230 y of computer model of a femur jigblank 50A. Furthermore, in some embodiments, the overestimation of theprocedure described below may be used to adjust the 3D surface model 40.

By selecting a femur jig blank 50A with an exterior portion 232 y closein size to the patient's lower femur end 204 y, the potential for anaccurate fit between the interior portion 230 y and the patient's femuris increased. Also, the amount of material that needs to be machined orotherwise removed from the jig blank 50A is reduced, thereby reducingmaterial waste and manufacturing time.

For a discussion of a method of selecting a jig blank 50 most closelycorresponding to the size and/or configuration of the patient's lowerfemur end, reference is first made to FIGS. 3-41L. FIG. 41H is a topperspective view of a left femoral cutting jig blank 50AL havingpredetermined dimensions. FIG. 41I is a bottom perspective view of thejig blank 50AL depicted in FIG. 41H. FIG. 41J is plan view of anexterior side or portion 232 y of the jig blank 50AL depicted in FIG.41H. FIG. 41K is a plurality of available sizes of left femur jig blanks50AL, each depicted in the same view as shown in FIG. 41J. FIG. 41L is aplurality of available sizes of right femur jig blanks 50AR, eachdepicted in the same view as shown in FIG. 41J.

A common jig blank 50, such as the left jig blank 50AL depicted in FIGS.41H-41J and intended for creation of a left femur jig that can be usedwith a patient's left femur, may include a posterior edge 240 y, ananterior edge 242 y, a lateral edge 244 y, a medial edge 246 y, alateral condyle portion 248 y, a medial condyle portion 250 y, theexterior side 232 y and the interior side 230 y. The jig blank 50AL ofFIGS. 41H-41J may be any one of a number of left femur jig blanks 50ALavailable in a limited number of standard sizes. For example, the jigblank 50AL of FIGS. 41H-41J may be an i-th left femur jig blank, wherei=1, 2, 3, 4, . . . . m and m represents the maximum number of leftfemur jig blank sizes.

As indicated in FIG. 41J, the anterior-posterior extent JAi of the jigblank 50AL is measured from the anterior edge 242 y to the posterioredge 240 y of the jig blank 50AL. The medial-lateral extent JMi of thejig blank 50AL is measured from the lateral edge 244 y to the medialedge 246 y of the jig blank 50AL.

As can be understood from FIG. 41K, a limited number of left femur jigblank sizes may be available for selection as the left femur jig blanksize to be machined into the left femur cutting jig 2A. For example, inone embodiment, there are nine sizes (m=9) of left femur jig blanks 50ALavailable. As can be understood from FIG. 41J, each femur jig blank 50ALhas an anterior-posterior/medial-lateral aspect ratio defined as JAi toJMi (e.g., “JAi/JMi” aspect ratio). Thus, as can be understood from FIG.41K, jig blank 50AL-1 has an aspect ratio defined as “JA₁/JM₁”, jigblank 50AL-2 has an aspect ratio defined as “JA₂/JM₂”, jig blank 50AL-3has an aspect ratio defined as “JA₃/JM₃”, jig blank 50AL-4 has an aspectratio defined as “JA₄/JM₄”, jig blank 50AL-5 has an aspect ratio definedas “JA₅/JM₅”, jig blank 50AL-6 has an aspect ratio defined as “JA₆/JM₆”,jig blank 50AL-7 has an aspect ratio defined as “JA₇/JM₇”, jig blank50AL-8 has an aspect ratio defined as “JA₈/JM₈”, and jig blank 50AL-9has an aspect ratio defined as “JA₉/JM₉”.

The jig blank aspect ratio is utilized to design left femur jigs 2Adimensioned specific to the patient's left femur features. In oneembodiment, the jig blank aspect ratio can be the exterior dimensions ofthe left femur jig 2A. In another embodiment, the jig blank aspect ratiocan apply to the left femur jig fabrication procedure for selecting theleft jig blank 50AL having parameters close to the dimensions of thedesired left femur jig 2A. This embodiment can improve the costefficiency of the left femur jig fabrication process because it reducesthe amount of machining required to create the desired jig 2 from theselected jig blank 50.

In FIG. 41K, the N−1 direction represents increasing jig aspect ratiosmoving from jig 50AL-3 to jig 50AL-2 to jig 50AL-1, where“JA₃/JM₃”<“JA₂/JM₂”<“JA₁/JM₁”. The increasing ratios of the jigs 50ALrepresent the corresponding increment of JAi values, where the jigs' JMivalues remain the same. In other words, since JA₃<JA₂<JA₁, andJM₃=JM₂=JM₁, then “JA₃/JM₃”<“JA₂/JM₂”<“JA₁/JM₁”. One example of theincrement level can be an increase from 5% to 20%.

The same rationale applies to the N-2 direction and the N-3 direction.For example, the N-2 direction represents increasing jig aspect ratiosfrom jig 50AL-6 to jig 50AL-5 to jig 50AL-4, where“JA₄/JM₄”<“JA₅/JM₅”<“JA₆/JM₆”. The increasing ratios of the jigs 50ALrepresent the corresponding increment of JAi values, where the JMivalues remain the same. The N-3 direction represents increasing jigaspect ratios from jig 50AL-9 to jig 50AL-8 to jig 50AL-7, where“JA₇/JM₇”<“JA₈/JM₈”<“JA₉/JM₉”. The increasing ratios of the jigs 50ALrepresent the corresponding increment of JAi values, where the JMivalues remain the same.

As can be understood from the plot 300 y depicted in FIG. 42C anddiscussed later in this Detailed Discussion, the E-1 directioncorresponds to the sloped line joining Group 1, Group 4 and Group 7.Similarly, the E-2 direction corresponds to the sloped line joiningGroup 2, Group 5 and Group 8. Also, the E-3 direction corresponds to thesloped line joining Group 3, Group 6 and Group 9.

As indicated in FIG. 41K, along direction E-2, the jig aspect ratiosremain the same among jigs 50AL-2, 50AL-5 and jig 50AL-8, where“JA₂/JM₂”=“JA₅/JM₅”=“JA₈/JM₈”. However, comparing to jig 50AL-2, jig50AL-5 is dimensioned larger and longer than jig 50AL-2. This is becausethe JA₅ value for jig 50AL-5 increases proportionally with the incrementof its JM₅ value in certain degrees in all X, Y, and Z-axis directions.In a similar fashion, jig 50AL-8 is dimensioned larger and longer thanjig 50AL-5 because the JA₈ increases proportionally with the incrementof its JM₈ value in certain degrees in all X, Y, and Z-axis directions.One example of the increment can be an increase from 5% to 20%.

The same rationale applies to directions E-1 and E-3. For example, inE-3 direction the jig ratios remain the same among the jigs 50AL-3,50AL-6 and jig 50AL-9. Compared to jig 50AL-3, jig 50AL-6 is dimensionedbigger and longer because both JM₆ and JA₆ values of jig 50AL-6 increaseproportionally in all X, Y, and Z-axis directions. Compared to jig50AL-6, jig 50AL-9 is dimensioned bigger and longer because both JM₉ andJA₉ values of jig 50AL-9 increase proportionally in all X, Y, andZ-axis.

As can be understood from FIG. 41L, a limited number of right femur jigblank sizes may be available for selection as the right femur jig blanksize to be machined into the right femur cutting jig 2A. For example, inone embodiment, there are nine sizes (m=9) of right femur jig blanks50AR available. As can be understood from FIG. 3, each femur jig blank50AR has an anterior-posterior/medial-lateral aspect ratio defined asJAi to JMi (e.g., “JAi/JMi” aspect ratio). Thus, as can be understoodfrom FIG. 41L, jig blank 50AR-1 has an aspect ratio defined as“JA₁/JM₁”, jig blank 50AR-2 has an aspect ratio defined as “JA₂/JM₂”,jig blank 50AR-3 has an aspect ratio defined as “JA₃/JM₃”, jig blank50AR-4 has an aspect ratio defined as “JA₄/JM₄”, jig blank 50AR-5 has anaspect ratio defined as “JA₅/JM₅”, jig blank 50AR-6 has an aspect ratiodefined as “JA₆/JM₆”, jig blank 50AR-7 has an aspect ratio defined as“JA₇/JM₇”, jig blank 50AR-8 has an aspect ratio defined as “JA₈/JM₈”,and jig blank 50AR-9 has an aspect ratio defined as “JA₉/JM₉”.

The jig blank aspect ratio may be utilized to design right femur jigs 2Adimensioned specific to the patient's right femur features. In oneembodiment, the jig blank aspect ratio can be the exterior dimensions ofthe right femur jig 2A. In another embodiment, the jig blank aspectratio can apply to the right femur jig fabrication procedure forselecting the right jig blank 50AR having parameters close to thedimensions of the desired right femur jig 2A. This embodiment canimprove the cost efficiency of the right femur jig fabrication processbecause it reduces the amount of machining required to create thedesired jig 2 from the selected jig blank 50.

In FIG. 41L, the N−1 direction represents increasing jig aspect ratiosmoving from jig 50AR-3 to jig 50AR-2 to jig 50AR-1, where“JA₃/JM₃”<“JA₂/JM₂”<“JA₁/JM₁”. The increasing ratios of the jigs 50ARrepresent the corresponding increment of JAi values, where the jigs' JMivalues remain the same. In other words, since JA₃<JA₂<JA₁, andJM₃=JM₂=JM₁, then “JA₃/JM₃”<“JA₂/JM₂”<“JA₁/JM₁”. One example of theincrement level can be an increase from 5% to 20%.

The same rationale applies to the N-2 direction and the N-3 direction.For example, the N-2 direction represents increasing jig aspect ratiosfrom jig 50AR-6 to jig 50AR-5 to jig 50AR-4, where“JA₄/JM₄”<“JA₅/JM₅”<“JA₆/JM₆”. The increasing ratios of the jigs 50ARrepresent the corresponding increment of JAi values, where the JMivalues remain the same. The N-3 direction represents increasing jigaspect ratios from jig 50AR-9 to jig 50AR-8 to jig 50AR-7, where“JA₇/JM₇”<“JA₈/JM₈”<“JA₉/JM₈”. The increasing ratios of the jigs 50ARrepresent the corresponding increment of JAi values, where the JMivalues remain the same.

As indicated in FIG. 41L, along direction E-2, the jig aspect ratiosremain the same among jigs 50AR-2, 50AR-5 and jig 50AR-8, where“JA₂/JM₂”=“JA₅/JM₅”=“JA₈/JM₈”. However, comparing to jig 50AR-2, jig50AR-5 is dimensioned larger and longer than jig 50AR-2. This is becausethe JA₅ value for jig 50AR-5 increases proportionally with the incrementof its JM₅ value in certain degrees in all X, Y, and Z-axis directions.In a similar fashion, jig 50AR-8 is dimensioned larger and longer thanjig 50AR-5 because the JA₈ increases proportionally with the incrementof its JM₈ value in certain degrees in all X, Y, and Z-axis directions.One example of the increment can be an increase from 5% to 20%.

The same rationale applies to directions E-1 and E-3. For example, inE-3 direction the jig ratios remain the same among the jigs 50AR-3,50AR-6 and jig 50AR-9. Compared to jig 50AR-3, jig 50AR-6 is dimensionedbigger and longer because both JM₆ and JA₆ values of jig 50AR-6 increaseproportionally in all X, Y, and Z-axis directions. Compared to jig50AR-6, jig 50AR-9 is dimensioned bigger and longer because both JM₉ andJA₉ values of jig 50AR-9 increase proportionally in all X, Y, andZ-axis.

The dimensions of the lower or knee joint forming end 204 y of thepatient's femur 18 can be determined by analyzing the 3D surface model40 or 3D arthritic model 36 in a manner similar to those discussed withrespect to the jig blanks 50. For example, as depicted in FIG. 42A,which is an axial view of the 3D surface model 40 or arthritic model 36of the patient's left femur 18 as viewed in a direction extending distalto proximal, the lower end 204 y of the surface model 40 or arthriticmodel 36 may include an anterior edge 262 y, a posterior edge 260 y, amedial edge 264 y, a lateral edge 266 y, a medial condyle 268 y, and alateral condyle 270 y. The femur dimensions may be determined for thebottom end face or tibia articulating surface 204 y of the patient'sfemur 18 via analyzing the 3D surface model 40 of the 3D arthritic model36. These femur dimensions can then be utilized to configure femur jigdimensions and select an appropriate femur jig.

As shown in FIG. 42A, the anterior-posterior extent fAP of the lower end204 y of the patient's femur 18 (i.e., the lower end 204 y of thesurface model 40 of the arthritic model 36, whether formed via open orclosed-loop analysis) is the length measured from the anterior edge 262y of the femoral lateral groove to the posterior edge 260 y of thefemoral lateral condyle 270 y. The medial-lateral extent fML of thelower end 204 y of the patient's femur 18 is the length measured fromthe medial edge 264 y of the medial condyle 268 y to the lateral edge266 y of the lateral condyle 270 y.

In one embodiment, the anterior-posterior extent fAP and medial-lateralextent fML of the femur lower end 204 y can be used for an aspect ratiofAP/fML of the femur lower end. The aspect ratios fAP/fML of a largenumber (e.g., hundreds, thousands, tens of thousands, etc.) of patientknees can be compiled and statistically analyzed to determine the mostcommon aspect ratios for jig blanks that would accommodate the greatestnumber of patient knees. This information may then be used to determinewhich one, two, three, etc. aspect ratios would be most likely toaccommodate the greatest number of patient knees.

The system 4 analyzes the lower ends 204 y of the patient's femur 18 asprovided via the surface model 40 of the arthritic model 36 (whether thearthritic model 36 is an 3D surface model generated via an open-loop ora 3D volumetric solid model generated via a closed-loop process) toobtain data regarding anterior-posterior extent fAP and medial-lateralextent fML of the femur lower ends 204 y. As can be understood from FIG.42B, which depicts the selected model jig blank 50AL of FIG. 41Jsuperimposed on the model femur lower end 204 y of FIG. 42A, the femurdimensional extents fAP, fML are compared to the jig blank dimensionalextents jAP, jML to determine which jig blank model to select as thestarting point for the machining process and the exterior surface modelfor the jig model.

As shown in FIG. 42B, a prospective left femoral jig blank 50AL issuperimposed to mate with the left femur lower end 204 y of thepatient's anatomical model as represented by the surface model 40 orarthritic model 36. The jig blank 50AL covers most of medial condyle 268y and the lateral condyle 270 y, leaving small exposed condyle regionsincluding t1, t2, t3. The medial medial-lateral condyle region t1represents the region between the medial edge 264 y of the medialcondyle 268 y and the medial edge 246 y of the jig blank 50AL. Thelateral medial-lateral condyle region t2 represents the region betweenthe lateral edge 266 y of the lateral condyle 270 y and the lateral edge244 y of the jig blank 50AL. The posterior anterior-posterior region t3represents the condyle region between the posterior edge 260 y of thelateral condyle 270 y and the posterior edge 240 y of the jig blank50AL.

The anterior edge 242 y of the jig blank 50AL extends past the anterioredge 262 y of the left femur lower end 204 y as indicated by anterioranterior-posterior overhang t4. Specifically, the anterioranterior-posterior overhang t4 represents the region between theanterior edge 262 y of the lateral groove of femur lower end 204 y andthe anterior edge 242 y of the jig blank 50AL. By obtaining andemploying the femur anterior-posterior fAP data and the femurmedial-lateral fML data, the system 4 can size the femoral jig blank50AL according to the following formulas: as jFML=fML−t1−t2 andjFAP=fAP−t3+t4, wherein jFML is the medial-lateral extent of the femurjig blank 50AL and jFAP is the anterior-posterior extent of the femurjig blank 50AL. In one embodiment, t1, t2, t3 and t4 will have thefollowing ranges: 2 mm≤t1≤6 mm; 2 mm t2≤6 mm; 2 mm t3≤12 mm; and 15 mmt4≤25 mm. In another embodiment, t1, t2, t3 and t4 will have thefollowing values: t1=3 mm; t2=3 mm; t3=6 mm; and t4=20 mm.

FIG. 42C is an example scatter plot 300 y for selecting from a pluralityof candidate jig blanks sizes a jig blank size appropriate for the lowerend 204 y of the patient's femur 18. In one embodiment, the X-axisrepresents the patient's femoral medial-lateral length fML inmillimeters, and the Y-axis represents the patient's femoralanterior-posterior length fAP in millimeters. In one embodiment, theplot is divided into a number of jig blank size groups, where each groupencompasses a region of the plot 300 y and is associated with specificparameters JM_(r), JA_(r) of a specific candidate jig blank size.

In one embodiment, the example scatter plot 300 y depicted in FIG. 42Chas nine jig blank size groups, each group pertaining to a singlecandidate jig blank size. However, depending on the embodiment, ascatter plot 300 y may have a greater or lesser number of jig blank sizegroups. The higher the number of jig blank size groups, the higher thenumber of the candidate jig blank sizes and the more dimension specifica selected candidate jig blank size will be to the patient's kneefeatures and the resulting jig 2. The more dimension specific theselected candidate jig blank size, the lower the amount of machiningrequired to produce the desired jig 2 from the selected jig blank 50.

Conversely, the lower the number of jig blank size groups, the lower thenumber of candidate jig blank sizes and the less dimension specific aselected candidate jig blank size will be to the patient's knee featuresand the resulting jig 2. The less dimension specific the selectedcandidate jig blank size, the higher the amount of machining required toproduce the desired jig 2 from the selected jig blank 50, adding extraroughing during the jig fabrication procedure.

As can be understood from FIG. 42C, in one embodiment, the nine jigblank size groups of the plot 300 y have the parameters JM_(r), JA_(r)as follows. Group 1 has parameters JM₁, JA₁. JM₁ represents themedial-lateral extent of the first femoral jig blank size, whereinJM₁=70 mm. JA₁ represents the anterior-posterior extent of the firstfemoral jig blank size, wherein JA₁=70.5 mm. Group 1 covers thepatient's femur fML and fAP data wherein 55 mm<fML<70 mm and 61mm<fAP<70.5 mm.

Group 2 has parameters JM₂, JA₂. JM₂ represents the medial-lateralextent of the second femoral jig blank size, wherein JM₂=70 mm. JA₂represents the anterior-posterior extent of the second femoral jig blanksize, wherein JA₂=61.5 mm. Group 2 covers the patient's femur fML andfAP data wherein 55 mm<fML<70 mm and 52 mm<fAP<61.5 mm.

Group 3 has parameters JM₃, JA₃. JM₃ represents the medial-lateralextent of the third femoral jig blank size, wherein JM₃=70 mm. JA₃represents the anterior-posterior extent of the third femoral jig blanksize, wherein JA₃=52 mm. Group 3 covers the patient's femur fML and fAPdata wherein 55 mm<fML<70 mm and 40 mm<fAP<52 mm.

Group 4 has parameters JM₄, JA₄. JM₄ represents the medial-lateralextent of the fourth femoral jig blank size, wherein JM₄=85 mm. JA₄represents the anterior-posterior extent of the fourth femoral jig blanksize, wherein JA₄=72.5 mm. Group 4 covers the patient's femur fML andfAP data wherein 70 mm<fML<85 mm and 63.5 mm<fAP<72.5 mm.

Group 5 has parameters JM₅, JA₅. JM₅ represents the medial-lateralextent of the fifth femoral jig blank size, wherein JM₅=85 mm. JA₅represents the anterior-posterior extent of the fifth femoral jig blanksize, wherein JA₅=63.5 mm. Group 5 covers the patient's femur fML andfAP data wherein 70 mm<fML<85 mm and 55 mm<fAP<63.5 mm.

Group 6 has parameters JM₆, JA₆. JM₆ represents the medial-lateralextent of the sixth femoral jig blank size, wherein JM₆=85 mm. JA₆represents the anterior-posterior extent of the sixth femoral jig blanksize, wherein JA₆=55 mm. Group 6 covers the patient's femur fML and fAPdata wherein 70 mm<fML<85 mm and 40 mm<fAP<55 mm.

Group 7 has parameters JM₇, JA₇. JM₇ represents the medial-lateralextent of the seventh femoral jig blank size, wherein JM₇=100 mm. JA₇represents the anterior-posterior extent of the seventh femoral jigblank size, wherein JA₇=75 mm. Group 7 covers the patient's femur fMLand fAP data wherein 85 mm<fML<100 mm and 65 mm<fAP<75 mm.

Group 8 has parameters JM₈, JA₈. JM₈ represents the medial-lateralextent of the eighth femoral jig blank size, wherein JM₈=100 mm. JA₈represents the anterior-posterior extent of the eighth femoral jig blanksize, wherein JA₈=65 mm. Group 8 covers the patient's femur fML and fAPdata wherein 85 mm<fML<100 mm and 57.5 mm<fAP<65 mm.

Group 9 has parameters JM₉, JA₉. JM₉ represents the medial-lateralextent of the ninth femoral jig blank size, wherein JM₉=100 mm. JA₉represents the anterior-posterior extent of the ninth femoral jig blanksize, wherein JA₉=57.5 mm. Group 9 covers the patient's femur fML andfAP data wherein 85 mm<fML<100 mm and 40 mm<fAP<57.5 mm.

As can be understood from FIG. 42D, which is a flow diagram illustratingan embodiment of a process of selecting an appropriately sized jigblank, bone anterior-posterior and medial-lateral extents fAP, fML aredetermined for the lower end 204 y of the surface model 40 of thearthritic model 36 [block 2000]. The bone extents fAP, fML of the lowerend 204 y are mathematically modified according to the above discussedjFML and jFAP formulas to arrive at the minimum femur jig blankanterior-posterior extent jFAP and medial-lateral extent jFML [block2010]. The mathematically modified bone extents fAP, fML or, morespecifically, the minimum femur jig blank anterior-posterior andmedial-lateral extents jFAP, jFML are referenced against the jig blankdimensions in the plot 300 y of FIG. 42C [block 2020]. The plot 300 ymay graphically represent the extents of candidate femur jig blanksforming a jig blank library. The femur jig blank 50A is selected to bethe jig blank size having the smallest extents that are stillsufficiently large to accommodate the minimum femur jig blankanterior-posterior and medial-lateral extents JFAP, jFML [block 2030].

In one embodiment, the exterior of the selected jig blank size is usedfor the exterior surface model of the jig model, as discussed below. Inone embodiment, the selected jig blank size corresponds to an actual jigblank that is placed in the CNC machine and milled down to the minimumfemur jig blank anterior-posterior and medial-lateral extents jFAP, jFMLto machine or otherwise form the exterior surface of the femur jig 2A.

The method outlined in FIG. 42D and in reference to the plot 300 y ofFIG. 42C can be further understood from the following example. Asmeasured in FIG. 42B with respect to the lower end 204 y of thepatient's femur 18, the extents of the patient's femur are as follows:fML=79.2 mm and fAP=54.5 mm [block 2000]. As previously mentioned, thelower end 204 y may be part of the surface model 40 of the arthriticmodel 36. Once the fML and fAP measurements are determined from thelower end 204 y, the corresponding jig jFML data and jig jFAP data canbe determined via the above-described jFML and jFAP formulas:jFML=fML−t1−t2, wherein t1=3 mm and t2=3 mm; and jFAP=fAP−t3+t4, whereint3=6 mm and t4=20 mm [block 2010]. The result of the jFML and jFAPformulas is jFML=73.2 mm and jFAP=68.5 mm.

As can be understood from the plot 300 y of FIG. 42C, the determined jigdata (i.e., jFML=73.2 mm and jFAP=68.5 mm) falls in Group 4 of the plot300 y. Group 4 has the predetermined femur jig blank parameters (JM₄,JA₄) of JM₄=85 mm and JA₄=72.5 mm. These predetermined femur jig blankparameters are the smallest of the various groups that are stillsufficiently large to meet the minimum femur blank extents jFAP, jFML[block 2020]. These predetermined femur jig blank parameters (JM₄=85 mmand JA₄=72.5 mm) may be selected as the appropriate femur jig blank size[block 2030].

In one embodiment, the predetermined femur jig blank parameters (85 mm,72.5 mm) can apply to the femur exterior jig dimensions as shown in FIG.41J. In other words, the jig blank exterior is used for the jig modelexterior as discussed with respect to FIGS. 42E-42I. Thus, the exteriorof the femur jig blank 50A undergoes no machining, and the unmodifiedexterior of the jig blank 50A with its predetermined jig blankparameters (85 mm, 72.5 mm) serves as the exterior of the finished femurjig 2A.

In another embodiment, the femur jig blank parameters (85 mm, 72.5 mm)can be selected for jig fabrication in the machining process. Thus, afemur jig blank 50A having predetermined parameters (85 mm, 72.5 mm) isprovided to the machining process such that the exterior of the femurjig blank 50A will be machined from its predetermined parameters (85 mm,72.5 mm) down to the desired femur jig parameters (73.2, 68.5 mm) tocreate the finished exterior of the femur jig 2A. As the predeterminedparameters (85 mm, 72.5 mm) are selected to be relatively close to thedesired femur jig parameters (73.2, 68.5 mm), machining time andmaterial waste are reduced.

While it may be advantageous to employ the above-described jig blankselection method to minimize material waste and machining time, in someembodiments, a jig blank will simply be provided that is sufficientlylarge to be applicable to all patient bone extents fAP, fML. Such a jigblank is then machined down to the desired jig blank extents jFAP, jFML,which serve as the exterior surface of the finished jig 2A.

In one embodiment, the number of candidate jig blank size groupsrepresented in the plot 300 y is a function of the number of jig blanksizes offered by a jig blank manufacturer. For example, a first plot 300y may pertain only to jig blanks manufactured by company A, which offersnine jig blank sizes. Accordingly, the plot 300 y has nine jig blanksize groups. A second plot 300 y may pertain only to jig blanksmanufactured by company B, which offers twelve jig blank size groups.Accordingly, the second plot 300 y has twelve jig blank size groups.

A plurality of candidate jig blank sizes exist, for example, in a jigblank library as represented by the plot 300 y of FIG. 42D. While eachcandidate jig blank may have a unique combination of anterior-posteriorand medial-lateral dimension sizes, in some embodiments, two or more ofthe candidate jig blanks may share a common aspect ratio jAP/jML orconfiguration. The candidate jig blanks of the library may be groupedalong sloped lines of the plot 300 y according to their aspect ratiosjAP/jML.

In one embodiment, the jig blank aspect ratio jAP/jML may be used totake a workable jig blank configuration and size it up or down to fitlarger or smaller individuals.

As can be understood from FIG. 42C, a series of 98 OA patients havingknee disorders were entered into the plot 300 y as part of a femur jigdesign study. Each patient's femur fAP and fML data was measured andmodified via the above-described jFML and jFAP formulas to arrive at thepatient's jig blank data (jFML, jFAP). The patient's jig blank data wasthen entered into the plot 300 y as a point. As can be understood fromFIG. 42C, no patient point lies outside the parameters of an availablegroup. Such a process can be used to establish group parameters and thenumber of needed groups.

In one embodiment, the selected jig blank parameters can be the femoraljig exterior dimensions that are specific to patient's knee features. Inanother embodiment, the selected jig blank parameters can be chosenduring fabrication process.

e. Formation of 3D Femoral Jig Model.

For a discussion of an embodiment of a method of generating a 3D femurjig model 346 y generally corresponding to the “integrated jig data” 48discussed with respect to [block 150] of FIG. 1E, reference is made toFIGS. 41H-41J, FIGS. 42E-42F, FIGS. 42G-42I and FIG. 42J-42K. FIGS.41H-41J are various views of a femur jig blank 50A. FIGS. 42E-42F are,respectively, exterior and interior perspective views of a femur jigblank exterior surface model 232M. FIGS. 42G and 42H are exteriorperspective views of the jig blank exterior model 232M and bone surfacemodel 40 being combined, and FIG. 42I is a cross section through thecombined models 232M, 40 as taken along section line 42I-421 in FIG.42H. FIGS. 42J and 42K are, respectively, exterior and interiorperspective views of the resulting femur jig model 346 y after having“saw cut and drill hole data” 44 integrated into the jig model 346 y tobecome an integrated or complete jig model 348 y generally correspondingto the “integrated jig data” 48 discussed with respect to [block 150] ofFIG. 1E.

As can be understood from FIGS. 41H-41J, the jig blank 50A, which hasselected predetermined dimensions as discussed with respect to FIG. 42C,includes an interior surface 230 y and an exterior surface 232 y. Theexterior surface model 232M depicted in FIGS. 42E and 42F is extractedor otherwise created from the exterior surface 232 y of the jig blankmodel 50A. Thus, the exterior surface model 232M is based on the jigblank aspect ratio of the femur jig blank 50A selected as discussed withrespect to FIG. 42C and is dimensioned specific to the patient's kneefeatures. The femoral jig surface model 232M can be extracted orotherwise generated from the jig blank model 50A of FIGS. 41H-41J byemploying any of the computer surface rendering techniques describedabove.

As can be understood from FIGS. 42G-42I, the exterior surface model 232Mis combined with the femur surface model 40 to respectively form theexterior and interior surfaces of the femur jig model 346 y. The femursurface model 40 represents the interior or mating surface of the femurjig 2A and corresponds to the femur arthroplasty target area 42. Thus,the model 40 allows the resulting femur jig 2A to be indexed to thearthroplasty target area 42 of the patient's femur 18 such that theresulting femur jig 2A will matingly receive the arthroplasty targetarea 42 during the arthroplasty procedure. The two surface models 232M,40 combine to provide a patient-specific jig model 346 y formanufacturing the femur jig 2A. In some embodiments, thispatient-specific jig model 346 y may include one or more areas ofoverestimation (as described below) to accommodate for irregularities inthe patient's bone surface and/or limitations in jig manufacturingcapabilities.

As can be understood from FIGS. 42H and 42I, once the models 232M, 40are properly aligned, a gap will exist between the two models 232M, 40.An image sewing method or image sewing tool is applied to the alignedmodels 232M, 40 to join the two surface models together to form the 3Dcomputer generated jig model 346 y of FIG. 42H into a single-piece,joined-together, and filled-in jig model 346 y similar in appearance tothe integrated jig model 348 y depicted in FIGS. 42J and 42K. In oneembodiment, the jig model 346 y may generally correspond to thedescription of the “jig data” 46 discussed with respect [block 145] ofFIG. 1E.

As can be understood from FIGS. 42H and 42I, the geometric gaps betweenthe two models 232M, 40, some of which are discussed below with respectto thicknesses P₁, P₂ and P₃, may provide certain space between the twosurface models 232M, 40 for slot width and length and drill bit lengthfor receiving and guiding cutting tools during TKA surgery. Because theresulting femur jig model 348 y depicted in FIGS. 42J and 42K may be a3D volumetric model generated from 3D surface models 232M, 40, a spaceor gap should be established between the 3D surface models 232M, 40.This allows the resulting 3D volumetric jig model 348 y to be used togenerate an actual physical 3D volumetric femur jig 2.

In some embodiments, the image processing procedure may include a modelrepair procedure for repairing the jig model 346 y after alignment ofthe two models 232M, 40. For example, various methods of the modelrepairing include, but are not limit to, user-guided repair, crackidentification and filling, and creating manifold connectivity, asdescribed in: Nooruddin et al., Simplification and Repair of PolygonalModels Using Volumetric Techniques (IEEE Transactions on Visualizationand Computer Graphics, Vol. 9, No. 2, April-June 2003); C. Erikson,Error Correction of a Large Architectural Model: The Henderson CountyCourthouse (Technical Report TR95-013, Dept. of Computer Science, Univ.of North Carolina at Chapel Hill, 1995); D. Khorramabdi, A Walk throughthe Planned CS Building (Technical Report UCB/CSD 91/652, ComputerScience Dept., Univ. of California at Berkeley, 1991); Morvan et al.,IVECS: An Interactive Virtual Environment for the Correction of .STLfiles (Proc. Conf. Virtual Design, August 1996); Bohn et al., ATopology-Based Approach for Shell-Closure, Geometric Modeling forProduct Realization, (P. R. Wilson et al., pp. 297-319, North-Holland,1993); Barequet et al., Filling Gaps in the Boundary of a Polyhedron,Computer Aided Geometric Design (vol. 12, no. 2, pp. 207-229, 1995);Barequet et al., Repairing CAD Models (Proc. IEEE Visualization '97, pp.363-370, October 1997); and Gueziec et al., Converting Sets of Polygonsto Manifold Surfaces by Cutting and Stitching, (Proc. IEEE Visualization1998, pp. 383-390, October 1998). Each of these references isincorporated into this Detailed Description in their entireties.

As can be understood from FIGS. 42J and 42K, the integrated jig model348 y may include several features based on the surgeon's needs. Forexample, the jig model 348 y may include a slot feature 30 for receivingand guiding a bone saw and drill holes 32 for receiving and guiding bonedrill bits. As can be understood from FIGS. 42H and 42I, to providesufficient structural integrity to allow the resulting femur jig 2A tonot buckle or deform during the arthroplasty procedure and to adequatelysupport and guide the bone saw and drill bits, the gap 350 y between themodels 232M, 40 may have the following offsets P₁, P₂, and P₃.

As can be understood from FIGS. 42H-42K, in one embodiment, thickness P₁extends along the length of the anterior drill holes 45N between themodels 232M, 40 and is for supporting and guiding a bone drill receivedtherein during the arthroplasty procedure. Thickness P₁ may be at leastapproximately four millimeters or at least approximately fivemillimeters thick. The diameter of the anterior drill holes 45N may beconfigured to receive a cutting tool of at least one-third inches.

Thickness P₂ extends along the length of a saw slot 30 between themodels 232M, 40 and is for supporting and guiding a bone saw receivedtherein during the arthroplasty procedure. Thickness P₂ may be at leastapproximately 10 mm or at least 15 mm thick.

Thickness P₃ extends along the length of the posterior drill holes 32Pbetween the models 232M, 40 and is for supporting and guiding a bonedrill received therein during the arthroplasty procedure. Thickness P₃may be at least approximately five millimeters or at least eightmillimeters thick. The diameter of the drill holes 32 may be configuredto receive a cutting tool of at least one-third inches.

In addition to providing sufficiently long surfaces for guiding drillbits or saws received therein, the various thicknesses P₁, P₂, P₃ arestructurally designed to enable the femur jig 2A to bear vigorous femurcutting, drilling and reaming procedures during the TKR surgery.

As indicated in FIGS. 42J and 42K, the integrated jig model 348 y mayinclude: feature 400 y that matches the patient's distal portion of themedial condyle cartilage; feature 402 y that matches the patient'sdistal portion of the lateral condyle cartilage; projection 404 y thatcan be configured as a contact or a hook and may securely engage theresulting jig 2A onto the patient's anterior femoral joint surfaceduring the TKR surgery; and the flat surface 406 y that provides ablanked labeling area for listing information regarding the patient,surgeon or/and the surgical procedure. Also, as discussed above, theintegrated jig model 348 y may include the saw cut slot 30 and the drillholes 32. The inner portion or side 100 of the jig model 348 y (and theresulting femur jig 2A) is the femur surface model 40, which willmatingly receive the arthroplasty target area 42 of the patient's femur18 during the arthroplasty procedure. In some embodiments, theoverestimation of the procedure described below may be used to adjustthe 3D surface model 40.

As can be understood by referring to [block 105] of FIG. 1B and FIGS.41A-41F, in one embodiment when cumulating the image scans 16 togenerate the one or the other of the models 40, 22, the models 40, 22are referenced to point P, which may be a single point or a series ofpoints, etc. to reference and orient the models 40, 22 relative to themodels 22, 28 discussed with respect to FIG. 1C and utilized for POP.Any changes reflected in the models 22, 28 with respect to point P(e.g., point P becoming point P′) on account of the POP is reflected inthe point P associated with the models 40, 22 (see [block 135] of FIG.1D). Thus, as can be understood from [block 140] of FIG. 1D and FIGS.42G-42I, when the jig blank exterior surface model 232M is combined withthe surface model 40 (or a surface model developed from the arthriticmodel 22) to create the jig model 346 y, the jig model 346 y isreferenced and oriented relative to point P′ and is generally equivalentto the “jig data” 46 discussed with respect to [block 145] of FIG. 1E.

Because the jig model 346 y is properly referenced and oriented relativeto point P′, the “saw cut and drill hole data” 44 discussed with respectto [block 125] of FIG. 1E can be properly integrated into the jig model346 y to arrive at the integrated jig model 348 y depicted in FIGS.42J-42K. The integrated jig model 348 y includes the saw cuts 30, drillholes 32 and the surface model 40. Thus, the integrated jig model 348 yis generally equivalent to the “integrated jig data” 48 discussed withrespect to [block 150] of FIG. 1E.

As can be understood from FIG. 42L, which illustrates a perspective viewof the integrated jig model 348 y mating with the “arthritic model” 22,the interior surface 40 of the jig model 348 y matingly receives thearthroplasty target area 42 of the femur lower end 204 y such that thejig model 348 y is indexed to mate with the area 42. (In someembodiments, the interior surface 40 includes areas of overestimation,described below, to accommodate for irregularities in the patient's bonesurface.) Because of the referencing and orientation of the variousmodels relative to the points P, P′ throughout the procedure, the sawcut slot 30 and drill holes 32 are properly oriented to result in sawcuts and drill holes that allow a resulting femur jig 2A to restore apatient's joint to a pre-degenerated or natural alignment condition.

As indicated in FIG. 42L, the integrated jig model 348 y may include ajig body 500 y, a projection 502 y on one side, and two projections 504y, 506 y the other side of jig body 500 y. The projections 504 y, 506 ymatch the medial and lateral condyle cartilage. The projections 502 y,504 y, 506 y extend integrally from the two opposite ends of the jigbody 500 y.

As can be understood from [blocks 155-165] of FIG. 1E, the integratedjig 348 y or, more specifically, the integrated jig data 48 can be sentto the CNC machine 10 to machine the femur jig 2A from the selected jigblank 50A. For example, the integrated jig data 48 may be used toproduce a production file that provides automated jig fabricationinstructions to a rapid production machine 10, as described in thevarious Park patent applications referenced above. The rapid productionmachine 10 then fabricates the patient-specific arthroplasty femur jig2A from the femur jig blank 50A according to the instructions.

The resulting femur jig 2A may have the features of the integrated jigmodel 348 y. Thus, as can be understood from FIG. 42L, the resultingfemur jig 2A may have the slot 30 and the drilling holes 32 formed onthe projections 502 y, 504 y, 506 y, depending on the needs of thesurgeon. The drilling holes 32 are configured to prevent the possibleIR/ER (internal/external) rotational axis misalignment between thefemoral cutting jig 2A and the patient's damaged joint surface duringthe distal femur cut portion of the TKR procedure. The slot 30 isconfigured to accept a cutting instrument, such as a reciprocating slawblade for transversely cutting during the distal femur cut portion ofthe TKR.

f. Defining a 3D Surface Model of an Arthroplasty Target Area of a TibiaUpper End for Use as a Surface of an Interior Portion of a TibiaArthroplasty Cutting Jig.

For a discussion of a method of generating a 3D model 40 of a targetarea 42 of a damaged upper end 604 y of a patient's tibia 20, referenceis made to FIGS. 43A-43C. FIG. 43A is an anterior-posterior (“AP”) imageslice 608 y of the damaged upper or knee joint end 604 y of thepatient's tibia 20, wherein the image slice 608 y includes an open-loopcontour line segment 610 y corresponding to the target area 42 of thedamaged upper end 604 y. FIG. 43B is a plurality of image slices (16-1,16-1, 16-2, . . . 16-n) with their respective open-loop contour linesegments (610 y-1, 610 y-2, . . . 610 y-n), the open-loop contour linesegments 610 y being accumulated to generate the 3D model 40 of thetarget area 42. FIG. 43C is a 3D model 40 of the target area 42 of thedamaged upper end 604 y as generated using the open-loop contour linesegments (16-1, 16-2, . . . 16-n) depicted in FIG. 43B.

As can be understood from FIGS. 1A, 1B and 43A, the imager 8 is used togenerate a 2D image slice 16 of the damaged upper or knee joint end 604y of the patient's tibia 20. As depicted in FIG. 43A, the 2D image 16may be an AP view of the tibia 20. Depending on whether the imager 8 isa MRI or CT imager, the image slice 16 will be a MRI or CT slice. Thedamaged upper end 604 y includes the tibia plateau 612 y, an anteriortibia shaft surface 614 y, and an area of interest or targeted area 42that extends along the tibia meniscus starting from a portion of thelateral tibia plateau surface to the anterior tibia surface 614 y. Thetargeted area 42 of the tibia upper end may be the articulating contactsurfaces of the tibia upper end that contact corresponding articulatingcontact surfaces of the femur lower or knee joint end.

As shown in FIG. 43A, the image slice 16 may depict the cancellous bone616 y, the cortical bone 618 y surrounding the cancellous bone, and thearticular cartilage lining portions of the cortical bone 618 y. Thecontour line 610 y may extend along the targeted area 42 and immediatelyadjacent the cortical bone and cartilage to outline the contour of thetargeted area 42 of the tibia upper end 604 y. The contour line 610 yextends along the targeted area 42 starting at point C on the lateral ormedial tibia plateau 612 y (depending on whether the slice 16 extendsthrough the lateral or medial portion of the tibia) and ends at point Don the anterior tibia shaft surface 614 y.

In one embodiment, as indicated in FIG. 43A, the contour line 610 yextends along the targeted area 42, but not along the rest of thesurface of the tibia upper end 604 y. As a result, the contour line 610y forms an open-loop that, as will be discussed with respect to FIGS.43B and 43C, can be used to form an open-loop region or 3D computermodel 40, which is discussed with respect to [block 140] of FIG. 1D andclosely matches the 3D surface of the targeted area 42 of the tibiaupper end. (In some embodiments, the 3D model 40 may be deliberatelyconfigured to be larger than the bone surface, in one or more areas, toaccommodate for irregularities. See description below in the context ofoverestimating the tibial mating surface.) Thus, in one embodiment, thecontour line is an open-loop and does not outline the entire corticalbone surface of the tibia upper end 604 y. Also, in one embodiment, theopen-loop process is used to form from the 2D images 16 a 3D surfacemodel 36 that generally takes the place of the arthritic model 36discussed with respect to [blocks 125-140] of FIG. 1D and which is usedto create the surface model 40 used in the creation of the “jig data” 46discussed with respect to [blocks 145-150] of FIG. 1E.

In one embodiment and in contrast to the open-loop contour line 610 ydepicted in FIGS. 43A and 43B, the contour line is a closed-loop contourline generally the same as the closed-loop contour line 210 y′ discussedwith respect to FIGS. 41D-41E, except the closed-loop contour linepertains to a tibia instead of a femur. Like the femur closed-loopcontour line discussed with respect to FIG. 41D, a tibia closed-loopcontour line may outline the entire cortical bone surface of the tibiaupper end and results in a closed-loop area. The tibia closed-loopcontour lines are combined in a manner similar that discussed withrespect to the femur contour lines in FIG. 41E. As a result, the tibiaclosed-loop area may require the analysis of the entire surface regionof the tibia upper end 604 y and result in the formation of a 3D modelof the entire tibia upper end 604 y in a manner similar to the femurlower end 204 y illustrated in FIG. 41F. Thus, the 3D surface modelresulting from the tibia closed-loop process ends up having in commonmuch, if not all, the surface of the 3D tibia arthritic model 36. In oneembodiment, the tibia closed-loop process may result in a 3D volumetricanatomical joint solid model from the 2D images 16 via applyingmathematical algorithms. U.S. Pat. No. 5,682,886, which was filed Dec.26, 1995 and is incorporated by reference in its entirety herein,applies a snake algorithm forming a continuous boundary or closed-loop.After the tibia has been outlined, a modeling process is used to createthe 3D surface model, for example, through a Bezier patches method.Other 3D modeling processes, e.g., commercially-available 3Dconstruction software as listed in other parts of this DetailedDescription, are applicable to 3D surface model generation forclosed-loop, volumetric solid modeling.

In one embodiment, the closed-loop process is used to form from the 2Dimages 16 a 3D volumetric solid model 36 that is essentially the same asthe arthritic model 36 discussed with respect to [blocks 125-140] ofFIG. 1D. The 3D volumetric solid model 36 is used to create the surfacemodel 40 used in the creation of the “jig data” 46 discussed withrespect to [blocks 145-150] of FIG. 1E.

The formation of a 3D volumetric solid model of the entire tibia upperend employs a process that may be much more memory and time intensivethan using an open-loop contour line to create a 3D model of thetargeted area 42 of the tibia upper end. Accordingly, although theclosed-loop methodology may be utilized for the systems and methodsdisclosed herein, for at least some embodiments, the open-loopmethodology may be preferred over the closed-loop methodology.

An example of a closed-loop methodology is disclosed in U.S. patentapplication Ser. No. 11/641,569 to Park, which is entitled “ImprovedTotal Joint Arthroplasty System” and was filed Jan. 19, 2007. Thisapplication is incorporated by reference in its entirety into thisDetailed Description.

As can be understood from FIGS. 43B and 41G, the imager 8 generates aplurality of image slices (16-1, 16-2 . . . 16-n) via repetitive imagingoperations [block 1000]. Each image slice 16 has an open-loop contourline (610 y-1, 610 y-2 . . . 610 y-n) extending along the targetedregion 42 in a manner as discussed with respect to FIG. 43A [block1005]. In one embodiment, each image slice is a two-millimeter 2D imageslice 16. The system 4 compiles the plurality of 2D image slices (16-1,16-2 . . . 16-n) and, more specifically, the plurality of open-loopcontour lines (610 y-1, 610 y-2, . . . 610 y-n) into the 3D femursurface computer model 40 depicted in FIG. 43C [block 1010]. Thisprocess regarding the generation of the surface model 40 is alsodiscussed in the overview section with respect to [blocks 100-105] ofFIG. 1B and [blocks 130-140] of FIG. 1D. A similar process may beemployed with respect to tibia closed-loop contour lines

As can be understood from FIG. 43C, the 3D tibia surface computer model40 is a 3D computer representation of the targeted region 42 of thetibia upper end. In one embodiment, the 3D representation of thetargeted region 42 is a 3D representation of the articulated femurcontact surfaces of the tibia proximal end. As the open-loop generated3D model 40 is a surface model of the relevant femur contacting portionsof the tibia upper end, as opposed to a 3D model of the entire surfaceof the tibia upper end as would be a result of a closed-loop contourline, the open-loop generated 3D model 40 is less time and memoryintensive to generate.

In one embodiment, the open-loop generated 3D model 40 is a surfacemodel of the femur facing end face of the tibia upper end, as opposed a3D model of the entire surface of the tibia upper end. The 3D model 40can be used to identify the area of interest or targeted region 42,which, as previously stated, may be the relevant femur contactingportions of the tibia upper end. Again, the open-loop generated 3D model40 is less time and memory intensive to generate as compared to a 3Dmodel of the entire surface of the tibia proximal end, as would begenerated by a closed-loop contour line. Thus, for at least someversions of the embodiments disclosed herein, the open-loop contour linemethodology is preferred over the closed-loop contour line methodology.However, the system 4 and method disclosed herein may employ either theopen-loop or closed-loop methodology and should not be limited to one orthe other.

Regardless of whether the 3D model 40 is a surface model of the targetedregion 42 (i.e., a 3D surface model generated from an open-loop processand acting as the arthritic model 22) or the entire femur facing endface of the tibia upper end (i.e., a 3D volumetric solid model generatedfrom a closed-loop process and acting as the arthritic model 22), thedata pertaining to the contour lines 610 y can be converted into the 3Dcontour computer model 40 via the surface rendering techniques disclosedin any of the aforementioned U.S. patent applications to Park. Forexample, surface rending techniques employed include point-to-pointmapping, surface normal vector mapping, local surface mapping, andglobal surface mapping techniques. Depending on the situation, one or acombination of mapping techniques can be employed.

In one embodiment, the generation of the 3D model 40 depicted in FIG.43C may be formed by using the image slices 16 to determine locationcoordinate values of each of a sequence of spaced apart surface pointsin the open-loop region of FIG. 43B. A mathematical model may then beused to estimate or compute the 3D model 40 in FIG. 43C. Examples ofother medical imaging computer programs that may be used include, butare not limited to: Analyze from AnalyzeDirect, Inc. of Overland Park,Kans.; open-source software such as Paraview of Kitware, Inc.; InsightToolkit (“ITK”) available at www.itk.org; 3D Slicer available atwww.slicer.org; and Mimics from Materialise of Ann Arbor, Mich.

Alternatively or additionally to the aforementioned systems forgenerating the 3D model 40 depicted in FIG. 43C, other systems forgenerating the 3D model 40 of FIG. 43C include the surface renderingtechniques of the Non-Uniform Rational B-spline (“NURB”) program or theBézier program. Each of these programs may be employed to generate the3D contour model 40 from the plurality of contour lines 610 y.

In one embodiment, the NURB surface modeling technique is applied to theplurality of image slices 16 and, more specifically, the plurality ofopen-loop contour lines 610 y of FIG. 41B. The NURB software generates a3D model 40 as depicted in FIG. 43C, wherein the 3D model 40 has areasof interest or targeted regions 42 that contain both a mesh and itscontrol points. For example, see Ervin et al., Landscape Modeling,McGraw-Hill, 2001, which is hereby incorporated by reference in itsentirety into this Detailed Description.

In one embodiment, the NURB surface modeling technique employs thefollowing surface equation:

${{G\left( {s,t} \right)} = \frac{\sum\limits_{i = 0}^{k\; 1}{\sum\limits_{j = 0}^{k\; 2}{{W\left( {i,j} \right)}{P\left( {i,j} \right)}{b_{i}(s)}{b_{j}(t)}}}}{\sum\limits_{i = 0}^{k\; 1}{\sum\limits_{j = 0}^{k\; 2}{{W\left( {i,j} \right)}{b_{i}(s)}{b_{j}(t)}}}}},$wherein P(i,j) represents a matrix of vertices with nrows=(k1+1) andncols=(k2+1), W(i,j) represents a matrix of vertex weights of one pervertex point, b_(i)(s) represents a row-direction basis or blending ofpolynomial functions of degree M1, b_(j)(t) represents acolumn-direction basis or blending polynomial functions of degree M2, srepresents a parameter array of row-direction knots, and t represents aparameter array of column-direction knots.

In one embodiment, the Bézier surface modeling technique employs theBézier equation (1972, by Pierre Bézier) to generate a 3D model 40 asdepicted in FIG. 43C, wherein the model 40 has areas of interest ortargeted regions 42. A given Bézier surface of order (n, m) is definedby a set of (n+1)(m+1) control points k_(i,j). It maps the unit squareinto a smooth-continuous surface embedded within a space of the samedimensionality as (k_(i,j)). For example, if k are all points in afour-dimensional space, then the surface will be within afour-dimensional space. This relationship holds true for aone-dimensional space, a two-dimensional space, a fifty-dimensionalspace, etc.

A two-dimensional Bézier surface can be defined as a parametric surfacewhere the position of a point p as a function of the parametriccoordinates u, v is given by:

${p\left( {u,v} \right)} = {\sum\limits_{i = 0}^{n}{\sum\limits_{j = 0}^{m}{{B_{i}^{n}(u)}{B_{j}^{m}(v)}k_{i,j}}}}$evaluated over the unit square, where

${B_{i}^{n}(u)} = {\begin{pmatrix}n \\i\end{pmatrix}{u^{i}\left( {1 - u} \right)}^{n - i}}$is a Bernstein polynomial and

$\begin{pmatrix}n \\i\end{pmatrix} = \frac{n!}{{i!}*{\left( {n - i} \right)!}}$is the binomial coefficient. See Grune et al, On Numerical Algorithm andInteractive Visualization for Optimal Control Problems, Journal ofComputation and Visualization in Science, Vol. 1, No. 4, July 1999,which is hereby incorporated by reference in its entirety into thisDetailed Description.

Various other surface rendering techniques are disclosed in otherreferences. For example, see the surface rendering techniques disclosedin the following publications: Lorensen et al., Marching Cubes: A highResolution 3d Surface Construction Algorithm, Computer Graphics, 21-3:163-169, 1987; Farin et al., NURB Curves &Surfaces: From ProjectiveGeometry to Practical Use, Wellesley, 1995; Kumar et al, RobustIncremental Polygon Triangulation for Surface Rendering, WSCG, 2000;Fleischer et al., Accurate Polygon Scan Conversion Using Half-OpenIntervals, Graphics Gems III, p. 362-365, code: p. 599-605, 1992; Foleyet al., Computer Graphics: Principles and Practice, Addison Wesley,1990; Glassner, Principles of Digital Image Synthesis, Morgan Kaufmann,1995, all of which are hereby incorporated by reference in theirentireties into this Detailed Description.

g. Selecting a Jig Blank Most Similar in Size and/or Configuration tothe Size of the Patient's Tibia Upper End.

As mentioned above, an arthroplasty jig 2, such as a tibia jig 2Bincludes an interior portion 104 and an exterior portion 106. The tibiajig 2B is formed from a tibia jig blank 50B, which, in one embodiment,is selected from a finite number of femur jig blank sizes. The selectionof the tibia jig blank 50B is based on a comparison of the dimensions ofthe patient's tibia upper end 604 y to the dimensions and/orconfigurations of the various sizes of tibia jig blanks 50B to selectthe tibia jig blank 50B most closely resembling the patient's tibiaupper end 604 y with respect to size and/or configuration. This selectedtibia jig blank 50B has an outer or exterior side or surface 632 y thatforms the exterior portion 632 y of the tibia jig 2B. The 3D surfacecomputer model 40 discussed with respect to the immediately precedingsection of this Detail Description is used to define a 3D surface 40into the interior side 630 y of the computer model of a tibia jig blank50B. Furthermore, in some embodiments, the overestimation of theprocedure described below may be used to adjust the 3D surface model 40.

By selecting a tibia jig blank 50B with an exterior portion 632 y closein size to the patient's upper tibia end 604 y, the potential for anaccurate fit between the interior portion 630 y and the patient's tibiais increased. Also, the amount of material that needs to be machined orotherwise removed from the jig blank 50B is reduced, thereby reducingmaterial waste and manufacturing time.

For a discussion of a method of selecting a jig blank 50 most closelycorresponding to the size and/or configuration of the patient's uppertibia end, reference is first made to FIGS. 43D-43H. FIG. 43D is a topperspective view of a right tibia cutting jig blank 50BR havingpredetermined dimensions. FIG. 43E is a bottom perspective view of thejig blank 50BR depicted in FIG. 43D. FIG. 43F is plan view of anexterior side or portion 232 y of the jig blank 50BR depicted in FIG.43D. FIG. 43G is a plurality of available sizes of right tibia jigblanks 50BR, each depicted in the same view as shown in FIG. 43F. FIG.43H is a plurality of available sizes of left tibia jig blanks, eachdepicted in the same view as shown in FIG. 43F.

A common jig blank 50, such as the right jig blank 50BR depicted inFIGS. 43D-43F and intended for creation of a right tibia jig that can beused with a patient's right tibia, may include a medial tibia footprojection 648 y for mating with the medial tibia plateau, a lateraltibia foot projection 650 y for mating with the lateral tibia plateau, aposterior edge 640 y, an anterior edge 642 y, a lateral edge 644 y, amedial edge 646 y, the exterior side 632 y and the interior side 630 y.The jig blank 50BR of FIGS. 43D-43F may be any one of a number of righttibia jig blanks 50BR available in a limited number of standard sizes.For example, the jig blank 50BR of FIGS. 43D-43F may be an i-th righttibia jig blank, where i=1, 2, 3, 4, . . . m and m represents themaximum number of right tibia jig blank sizes.

As indicated in FIG. 43F, the anterior-posterior extent TAi of the jigblank 50BR is measured from the anterior edge 642 y to the posterioredge 640 y of the jig blank 50BR. The medial-lateral extent TMi of thejig blank 50BR is measured from the lateral edge 644 y to the medialedge 646 y of the jig blank 50BR.

As can be understood from FIG. 43G, a limited number of right tibia jigblank sizes may be available for selection as the right tibia jig blanksize to be machined into the right tibia cutting jig 2B. For example, inone embodiment, there are three sizes (m=3) of right tibia jig blanks50BR available. As can be understood from FIG. 43F, each tibia jig blank50BR has an anterior-posterior/medial-lateral aspect ratio defined asTAi to TMi (e.g., “TAi/TMi” aspect ratio). Thus, as can be understoodfrom FIG. 43G, jig blank 50BR-1 has an aspect ratio defined as“TA₁/TM₁”, jig blank 50BR-2 has an aspect ratio defined as “TA₂/TM₂”,and jig blank 50BR-3 has an aspect ratio defined as “TA₃/TM₃”.

The jig blank aspect ratio is utilized to design right tibia jigs 2Bdimensioned specific to the patient's right tibia features. In oneembodiment, the jig blank aspect ratio can be the exterior dimensions ofthe right tibia jig 2B. In another embodiment, the jig blank aspectratio can apply to the right tibia jig fabrication procedure forselecting the right jig blank 50BR having parameters close to thedimensions of the desired right tibia jig 2B. This embodiment canimprove the cost efficiency of the right tibia jig fabrication processbecause it reduces the amount of machining required to create thedesired jig 2 from the selected jig blank 50.

In FIG. 43G there is a single jig blank aspect ratio depicted for thecandidate tibia jig blank sizes. In embodiments having a greater numberof jig blank aspect ratios for the candidate tibia jig blank sizes, FIG.43G would be similar to FIG. 41K and would have an N-1 direction, andpotentially N-2 and N-3 directions, representing increasing jig blankaspect ratios. The relationships between the various tibia jig blankaspect ratios would be similar to those discussed with respect to FIG.41K for the femur jig blank aspect ratios.

As can be understood from the plot 900 depicted in FIG. 17 and discussedlater in this Detailed Discussion, the E-1 direction corresponds to thesloped line joining Group 1, Group 2 and Group 3 in the plot 900.

As indicated in FIG. 43G, along direction E-1, the jig blank aspectratios remain the same among jigs blanks 50BR-1, 50BR-2 and 50BR-3,where “TA₁/TM₁”=“TA₂/TM₂”=“TA₃/TM₃”. However, comparing to jig blank50BR-1, jig blank 50BR-2 is dimensioned larger and longer than jig blank50BR-1. This is because the TA₂ value for jig blank 50BR-2 increasesproportionally with the increment of its TM₂ value in certain degrees inall X, Y, and Z-axis directions. In a similar fashion, jig blank 50BR-3is dimensioned larger and longer than jig blank 50BR-2 because the TA₃increases proportionally with the increment of its TM₃ value in certaindegrees in all X, Y, and Z-axis directions. One example of the incrementcan be an increase from 5% to 20%. In embodiments where there areadditional aspect ratios available for the tibia jig blank sizes, as wasillustrated in FIG. 41K with respect to the femur jig blank sizes, therelationship between tibia jig blank sizes may be similar to thatdiscussed with respect to FIGS. 41K and 43G.

As can be understood from FIG. 43H, a limited number of left tibia jigblank sizes may be available for selection as the left tibia jig blanksize to be machined into the left tibia cutting jig 2B. For example, inone embodiment, there are three sizes (m=3) of left tibia jig blanks50BL available. As can be understood from FIG. 43F, each tibia jig blank50BL has an anterior-posterior/medial-lateral aspect ratio defined asTAi to TMi (e.g., “TAi/TMi” aspect ratio). Thus, as can be understoodfrom FIG. 43H, jig blank 50BL-1 has an aspect ratio defined as“TA₁/TM₁”, jig blank 50BL-2 has an aspect ratio defined as “TA₂/TM₂”,and jig blank 50BL-3 has an aspect ratio defined as “TA₃/TM₃”.

The jig blank aspect ratio is utilized to design left tibia jigs 2Bdimensioned specific to the patient's left tibia features. In oneembodiment, the jig blank aspect ratio can be the exterior dimensions ofthe left tibia jig 2B. In another embodiment, the jig blank aspect ratiocan apply to the left tibia jig fabrication procedure for selecting theleft jig blank 50BL having parameters close to the dimensions of thedesired left tibia jig 2B. This embodiment can improve the costefficiency of the left tibia jig fabrication process because it reducesthe amount of machining required to create the desired jig 2 from theselected jig blank 50.

In FIG. 43H there is a single jig blank aspect ratio depicted for thecandidate tibia jig blank sizes. In embodiments having a greater numberof jig blank aspect ratios for the candidate tibia jig blank sizes, FIG.43H would be similar to FIG. 41L and would have an N-1 direction, andpotentially N-2 and N-3 directions, representing increasing jig blankaspect ratios. The relationships between the various tibia jig blankaspect ratios would be similar to those discussed with respect to FIG.41L for the femur jig blank aspect ratios.

As indicated in FIG. 43H, along direction E-1, the jig blank aspectratios remain the same among jigs blanks 50BL-1, 50BL-2 and 50BL-3,where “TA₁/TM₁”=“TA₂/TM₂”=“TA₃/TM₃”. However, comparing to jig blank50BL-1, jig blank 50BL-2 is dimensioned larger and longer than jig blank50BL-1. This is because the TA₂ value for jig blank 50BL-2 increasesproportionally with the increment of its TM₂ value in certain degrees inall X, Y, and Z-axis directions. In a similar fashion, jig blank 50BL-3is dimensioned larger and longer than jig blank 50BL-2 because the TA₃increases proportionally with the increment of its TM₃ value in certaindegrees in all X, Y, and Z-axis directions. One example of the incrementcan be an increase from 5% to 20%. In embodiments where there areadditional aspect ratios available for the tibia jig blank sizes, as wasillustrated in FIG. 41L with respect to the femur jig blank sizes, therelationship between tibia jig blank sizes may be similar to thatdiscussed with respect to FIGS. 41L and 43H.

The dimensions of the upper or knee joint forming end 604 y of thepatient's tibia 20 can be determined by analyzing the 3D surface model40 or 3D arthritic model 36 in a manner similar to those discussed withrespect to the jig blanks 50. For example, as depicted in FIG. 43I,which is an axial view of the 3D surface model 40 or arthritic model 36of the patient's right tibia 20 as viewed in a direction extendingproximal to distal, the upper end 604 y of the surface model 40 orarthritic model 36 may include an anterior edge 660 y, a posterior edge662 y, a medial edge 664 y and a lateral edge 666 y. The tibiadimensions may be determined for the top end face or femur articulatingsurface 604 y of the patient's tibia 20 via analyzing the 3D surfacemodel 40 of the 3D arthritic model 36. These tibia dimensions can thenbe utilized to configure tibia jig dimensions and select an appropriatetibia jig.

As shown in FIG. 43I, the anterior-posterior extent tAP of the upper end604 y of the patient's tibia 20 (i.e., the upper end 604 y of thesurface model 40 of the arthritic model 36, whether formed via open orclosed-loop analysis) is the length measured from the anterior edge 660y of the tibia plateau to the posterior edge 662 y of the tibia plateau.The medial-lateral extent tML of the upper end 604 y of the patient'stibia 20 is the length measured from the medial edge 664 y of the medialtibia plateau to the lateral edge 666 y of the lateral tibia plateau.

In one embodiment, the anterior-posterior extent tAP and medial-lateralextent tML of the tibia upper end 604 y can be used for an aspect ratiotAP/tML of the tibia upper end. The aspect ratios tAP/tML of a largenumber (e.g., hundreds, thousands, tens of thousands, etc.) of patientknees can be compiled and statistically analyzed to determine the mostcommon aspect ratios for jig blanks that would accommodate the greatestnumber of patient knees. This information may then be used to determinewhich one, two, three, etc. aspect ratios would be most likely toaccommodate the greatest number of patient knees.

The system 4 analyzes the upper ends 604 y of the patient's tibia 20 asprovided via the surface model 40 of the arthritic model 36 (whether thearthritic model 36 is an 3D surface model generated via an open-loop ora 3D volumetric solid model generated via a closed-loop process), toobtain data regarding anterior-posterior extent tAP and medial-lateralextent tML of the tibia upper ends 604 y. As can be understood from FIG.43J, which depicts the selected model jig blank 50BR of FIG. 43Fsuperimposed on the model tibia upper end 604 y of FIG. 43I, the tibiadimensional extents tAP, tML are compared to the jig blank dimensionalextents TAi, TMi to determine which jig blank model to select as thestarting point for the machining process and the exterior surface modelfor the jig model.

As shown in FIG. 43J, a prospective right tibia jig blank 50BR issuperimposed to mate with the right tibia upper end 604 y of thepatient's anatomical model as represented by the surface model 40 orarthritic model 36. In one embodiment, the jig blank 50BR may cover theanterior approximately two thirds of the tibia plateau, leaving theposterior approximately one third of the tibia exposed. Included in theexposed portion of the tibia plateau are lateral and medial exposedregions of the tibia plateau, as respectively represented by regions q1and q2 in FIG. 43J. Specifically, exposed region q1 is the region of theexposed tibia plateau between the tibia and jig blank lateral edges 666y, 644 y, and exposed region q2 is the region of the exposed tibiaplateau between the tibia and jig blank medial edges 664 y, 646 y.

By obtaining and employing the tibia anterior-posterior tAP data and thetibia medial-lateral tML data, the system 4 can size the tibia jig blank50BR according to the following formula: jTML=tML−q1−q2, wherein jTML isthe medial-lateral extent of the tibia jig blank 50BR. In oneembodiment, q1 and q2 will have the following ranges: 2 mm≤q1≤4 mm; and2 mm≤q2≤4 mm. In another embodiment, q1 will be approximately 3 mm andq2 will approximately 3 mm.

FIG. 43K is an example scatter plot 900 for selecting from a pluralityof candidate jig blanks sizes a jig blank size appropriate for the upperend 604 y of the patient's tibia 20. In one embodiment, the X-axisrepresents the patient's tibia medial-lateral length tML in millimeters,and the Y-axis represents the patient's tibia anterior-posterior lengthtAP in millimeters. In one embodiment, the plot 900 is divided into anumber of jig blank size groups, where each group encompasses a regionof the plot 900 and is associated with a specific parameter TM_(r) of aspecific candidate jig blank size.

In one embodiment, the example scatter plot 900 depicted in FIG. 43K hasthree jig blank size groups, each group pertaining to a single candidatejig blank size. However, depending on the embodiment, a scatter plot 900may have a greater or lesser number of jig blank size groups. The higherthe number of jig blank size groups, the higher the number of thecandidate jig blank sizes and the more dimension specific a selectedcandidate jig blank size will be to the patient's knee features and theresulting jig 2. The more dimension specific the selected candidate jigblank size, the lower the amount of machining required to produce thedesired jig 2 from the selected jig blank 50.

Conversely, the lower the number of jig blank size groups, the lower thenumber of candidate jig blank sizes and the less dimension specific aselected candidate jig blank size will be to the patient's knee featuresand the resulting jig 2. The less dimension specific the selectedcandidate jig blank size, the higher the amount of machining required toproduce the desired jig 2 from the selected jig blank 50, adding extraroughing during the jig fabrication procedure.

The tibia anterior-posterior length tAP may be relevant because it mayserve as a value for determining the aspect ratio TA_(i)/TM_(i). fortibia jig blanks 50B such as those discussed with respect to FIGS.43F-43H and 43K. Despite this, in some embodiments, tibiaanterior-posterior length TA_(i) of the candidate jig blanks may not bereflected in the plot 900 depicted in FIG. 43K or the relationshipdepicted in FIG. 43J because in a practical setting for someembodiments, tibia jig anterior-posterior length may be less significantthan tibia jig medial-lateral length. For example, although a patient'stibia anterior-posterior distance varies according to their kneefeatures, the length of the foot projection 800 y, 802 y (see FIG. 44G)of a tibia jig 2B is simply increased without the need to create a jigblank or jig that is customized to correspond to the tibiaanterior-posterior length TAi. In other words, in some embodiments, theonly difference in anterior-posterior length between various tibia jigsis the difference in the anterior-posterior length of their respectivefoot projections 800 y, 802 y.

In some embodiments, as can be understood from FIGS. 43J and 44I, theanterior-posterior length of a tibia jig 2B, with its foot projection800 y, 802 y, covers approximately half of the tibia plateau. Due inpart to this “half” distance coverage, which varies frompatient-to-patient by only millimeters to a few centimeter, in oneembodiment, the anterior-posterior length of the jig may not be of asignificant concern. However, because the jig may cover a substantialportion of the medial-lateral length of the tibia plateau, themedial-lateral length of the jig may be of substantial significance ascompared to the anterior-posterior length.

While in some embodiments the anterior-posterior length of a tibia jig2B may not be of substantial significance as compared to themedial-lateral length, in some embodiments the anterior-posterior lengthof the tibia jig is of significance. In such an embodiment, jig sizesmay be indicated in FIG. 43K by their aspect ratios TA_(i)/TM_(i) asopposed to just TM_(i). In other words, the jig sizes may be depicted inFIG. 43K in a manner similar to that depicted in FIG. 42C. Furthermore,in such embodiments, FIGS. 43G and 43H may have additional jig blankratios similar to that depicted in FIGS. 41K and 41L. As a result, theplot 900 of 43K may have additional diagonal lines joining the jig blanksizes belonging to each jig blank ratio in a manner similar to thatdepicted in plot 300 y of FIG. 42C. Also, in FIG. 43K and in a mannersimilar to that shown in FIG. 42C, there may be additional horizontallines dividing plot 900 according to anterior-posterior length torepresent the boundaries of the various jig blank sizes.

As can be understood from FIG. 43K, in one embodiment, the three jigblank size groups of the plot 900 have parameters TM_(r), TA_(r) asfollows. Group 1 has parameters TM₁, TA1. TM₁ represents themedial-lateral extent of the first tibia jig blank size, wherein TM₁=70mm. TA₁ represents the anterior-posterior extent of the first femoraljig blank size, wherein TA₁=62 mm. Group 1 covers the patient's tibiatML and tAP data wherein 55 mm<tML<70 mm and 45 mm<tAP<75 mm.

Group 2 has parameters TM₂, TA2. TM₂ represents the medial-lateralextent of the second tibia jig blank size, wherein TM₂=85 mm. TA₂represents the anterior-posterior extent of the second femoral jig blanksize, wherein TA₂=65 mm. Group 2 covers the patient's tibia tML and tAPdata wherein 70 mm<tML<85 mm and 45 mm<tAP<75 mm.

Group 3 has parameters TM₃, TA3. TM₃ represents the medial-lateralextent of the third tibia jig blank size, wherein TM₃=100 mm. TA₃represents the anterior-posterior extent of the second femoral jig blanksize, wherein TA₃=68.5 mm. Group 3 covers the patient's tibia tML andtAP data wherein 85 mm<tML<100 mm and 45 mm<tAP<75 mm.

In some embodiments and in contrast to the selection process for thefemur jig blanks discussed with respect to FIGS. 41H-42D, the tibia jigblank selection process discussed with respect to FIGS. 43D-43L may onlyconsider or employ the medial-lateral tibia jig value jTML and relatedmedial-lateral values TMi, tML. Accordingly, in such embodiments, theanterior-posterior tibia jig value JTAP and related anterior-posteriorvalues TAi, tAP for the tibia jig and tibia plateau are not considered.

As can be understood from FIG. 43L, which is a flow diagram illustratingan embodiment of a process of selecting an appropriately sized jigblank, the bone medial-lateral extent tML is determined for the upperend 604 y of the surface model 40 of the arthritic model 36 [block3000]. The medial-lateral bone extent tML of the upper end 604 y ismathematically modified according to the above discussed jTML formula toarrive at the minimum tibia jig blank medial-lateral extent jTML [block3010]. The mathematically modified bone medial-lateral extent tML or,more specifically, the minimum tibia jig blank medial-lateral extentjTML is referenced against the jig blank dimensions in the plot 900 ofFIG. 43K [block 3020]. The plot 900 may graphically represent theextents of candidate tibia jig blanks forming a jig blank library. Thetibia jig blank 50B is selected to be the jig blank size having thesmallest extents that are still sufficiently large to accommodate theminimum tibia jig blank medial-lateral extent jTML [block 3030].

In one embodiment, the exterior of the selected jig blank size is usedfor the exterior surface model of the jig model, as discussed below. Inone embodiment, the selected jig blank size corresponds to an actual jigblank that is placed in the CNC machine and milled down to the minimumtibia jig blank anterior-posterior and medial-lateral extents jTAP, jTMLto machine or otherwise form the exterior surface of the tibia jig 2B.

The method outlined in FIG. 43L and in reference to the plot 900 of FIG.43K can be further understood from the following example. As measured inFIG. 43J with respect to the upper end 604 y of the patient's tibia 20,the extents of the patient's tibia are as follows: tML=85.2 mm [block3000]. As previously mentioned, the upper end 604 y may be part of thesurface model 40 of the arthritic model 36. Once the tML measurement isdetermined from the upper end 604 y, the corresponding jig jTML data canbe determined via the above-described jTML formula: jTML=tML−q1−q2,wherein q1=3 mm and q2=3 mm [block 3010]. The result of the jTML formulais jTML=79.2 mm.

As can be understood from the plot 900 of FIG. 43K, the determined jigdata (i.e., jTML=79.2 mm) falls in Group 2 of the plot 900. Group 2 hasthe predetermined tibia jig blank parameters (TM₂) of TM₂=85 mm. Thispredetermined tibia jig blank parameter is the smallest of the variousgroups that are still sufficiently large to meet the minimum tibia blankextents jTML [block 3020]. This predetermined tibia jig blank parameters(TM₂=85 mm) may be selected as the appropriate tibia jig blank size[block 3030].

In one embodiment, the predetermined tibia jig blank parameter (85 mm)can apply to the tibia exterior jig dimensions as shown in FIG. 43F. Inother words, the jig blank exterior is used for the jig model exterioras discussed with respect to FIGS. 44A-44E. Thus, the exterior of thetibia jig blank 50B undergoes no machining, and the unmodified exteriorof the jig blank 50B with its predetermined jig blank parameter (85 mm)serves as the exterior of the finished tibia jig 2B.

In another embodiment, the tibia jig blank parameter (85 mm) can beselected for jig fabrication in the machining process. Thus, a tibia jigblank 50B having a predetermined parameter (85 mm) is provided to themachining process such that the exterior of the tibia jig blank 50B willbe machined from its predetermined parameter (85 mm) down to the desiredtibia jig parameter (79.2 mm) to create the finished exterior of thetibia jig 2B. As the predetermined parameter (85 mm) is selected to berelatively close to the desired femur jig parameter (79.2 mm), machiningtime and material waste are reduced.

While it may be advantageous to employ the above-described jig blankselection method to minimize material waste and machining time, in someembodiments, a jig blank will simply be provided that is sufficientlylarge to be applicable to all patient bone extents tML. Such a jig blankis then machined down to the desired jig blank extent jTML, which serveas the exterior surface of the finished jig 2B.

In one embodiment, the number of candidate jig blank size groupsrepresented in the plot 900 is a function of the number of jig blanksizes offered by a jig blank manufacturer. For example, a first plot 900may pertain only to jig blanks manufactured by company A, which offersthree jig blank sizes. Accordingly, the plot 900 has three jig blanksize groups. A second plot 900 may pertain only to jig blanksmanufactured by company B, which offers six jig blank size groups.Accordingly, the second plot 900 has six jig blank size groups.

A plurality of candidate jig blank sizes exist, for example, in a jigblank library as represented by the plot 900 of FIG. 43L. While eachcandidate jig blank may have a unique combination of anterior-posteriorand medial-lateral dimension sizes, in some embodiments, two or more ofthe candidate jig blanks may share a common aspect ratio tAP/tML orconfiguration. The candidate jig blanks of the library may be groupedalong sloped lines of the plot 900 according to their aspect ratiostAP/tML.

In one embodiment, the jig blank aspect ratio tAP/tML may be used totake a workable jig blank configuration and size it up or down to fitlarger or smaller individuals.

As can be understood from FIG. 43K, a series of 98 OA patients havingknee disorders were entered into the plot 900 as part of a tibia jigdesign study. Each patient's tibia tAP and tML data was measured. Eachpatient tibia tML data was modified via the above-described jTML formulato arrive at the patient's jig blank data (jFML). The patient's jigblank data was then entered into the plot 900 as a point. As can beunderstood from FIG. 43K, no patient point lies outside the parametersof an available group. Such a process can be used to establish groupparameters and the number of needed groups.

In one embodiment, the selected jig blank parameters can be the tibiajig exterior dimensions that are specific to patient's knee features. Inanother embodiment, the selected jig blank parameters can be chosenduring fabrication process.

h. Formation of 3D Tibia Jig Model.

For a discussion of an embodiment of a method of generating a 3D tibiajig model 746 y generally corresponding to the “integrated jig data” 48discussed with respect to [block 150] of FIG. 1E, reference is made toFIGS. 43D-43F, FIGS. 44A-44B, FIGS. 44C-44F and FIG. 44G-44H. FIGS.43D-43F are various views of a tibia jig blank 50B. FIGS. 44A-44B are,respectively, exterior and interior perspective views of a tibia jigblank exterior surface model 632M. FIGS. 44C-44F are exteriorperspective views of the tibia jig blank exterior model 632M and bonesurface model 40 being combined. FIGS. 44G and 44H are, respectively,exterior and interior perspective views of the resulting tibia jig model746 y after having “saw cut and drill hole data” 44 integrated into thejig model 746 y to become an integrated or complete jig model 748 ygenerally corresponding to the “integrated jig data” 48 discussed withrespect to [block 150] of FIG. 1E.

As can be understood from FIGS. 43D-43F, the jig blank 50B, which hasselected predetermined dimensions as discussed with respect to FIGS. 43Kand 43L, includes an interior surface 630 y and an exterior surface 632y. The exterior surface model 632M depicted in FIGS. 44A and 44B isextracted or otherwise created from the exterior surface 632 y of thejig blank model 50B. Thus, the exterior surface model 632M is based onthe jig blank aspect ratio of the tibia jig blank 50B selected asdiscussed with respect to FIGS. 43K and 43L and is dimensioned specificto the patient's knee features. The tibia jig surface model 632M can beextracted or otherwise generated from the jig blank model 50B of FIGS.43D-43F by employing any of the computer surface rendering techniquesdescribed above.

As can be understood from FIGS. 44C-44E, the exterior surface model 632Mis combined with the tibia surface model 40 to respectively form theexterior and interior surfaces of the tibia jig model 746 y. The tibiasurface model 40 represents the interior or mating surface of the tibiajig 2B and corresponds to the tibia arthroplasty target area 42. Thus,the model 40 allows the resulting tibia jig 2B to be indexed to thearthroplasty target area 42 of the patient's tibia 20 such that theresulting tibia jig 2B will matingly receive the arthroplasty targetarea 42 during the arthroplasty procedure. The two surface models 632M,40 combine to provide a patient-specific jig model 746 y formanufacturing the tibia jig 2B.

As can be understood from FIGS. 44D and 44E, once the models 632M, 40are properly aligned, a gap will exist between the two models 632M, 40.An image sewing method or image sewing tool is applied to the alignedmodels 632M, 40 to join the two surface models together to form the 3Dcomputer generated jig model 746 y of FIG. 44D into a single-piece,joined-together, and filled-in jig model 746 y similar in appearance tothe integrated jig model 748 y depicted in FIGS. 44G and 44H. In oneembodiment, the jig model 746 y may generally correspond to thedescription of the “jig data” 46 discussed with respect [block 145] ofFIG. 1E.

As can be understood from FIGS. 44D-44F, 44G and 44H, the geometric gapsbetween the two models 632M, 40, some of which are discussed below withrespect to thicknesses V₁, V₂ and V₃, may provide certain space betweenthe two surface models 632M, 40 for slot width and length and drill bitlength for receiving and guiding cutting tools during TKA surgery.Because the resulting tibia jig model 748 y depicted in FIGS. 44G and44H may be a 3D volumetric model generated from 3D surface models 632M,40, a space or gap should be established between the 3D surface models632M, 40. This allows the resulting 3D volumetric jig model 748 y to beused to generate an actual physical 3D volumetric tibia jig 2B.

In some embodiments, the image processing procedure may include a modelrepair procedure for repairing the jig model 746 y after alignment ofthe two models 632M, 40. For example, various methods of the modelrepairing include, but are not limit to, user-guided repair, crackidentification and filling, and creating manifold connectivity, asdescribed in: Nooruddin et al., Simplification and Repair of PolygonalModels Using Volumetric Techniques (IEEE Transactions on Visualizationand Computer Graphics, Vol. 9, No. 2, April-June 2003); C. Erikson,Error Correction of a Large Architectural Model: The Henderson CountyCourthouse (Technical Report TR95-013, Dept. of Computer Science, Univ.of North Carolina at Chapel Hill, 1995); D. Khorramabdi, A Walk throughthe Planned CS Building (Technical Report UCB/CSD 91/652, ComputerScience Dept., Univ. of California at Berkeley, 1991); Morvan et al.,IVECS: An Interactive Virtual Environment for the Correction of .STLfiles (Proc. Conf. Virtual Design, August 1996); Bohn et al., ATopology-Based Approach for Shell-Closure, Geometric Modeling forProduct Realization, (P. R. Wilson et al., pp. 297-319, North-Holland,1993); Barequet et al., Filling Gaps in the Boundary of a Polyhedron,Computer Aided Geometric Design (vol. 12, no. 2, pp. 207-229, 1995);Barequet et al., Repairing CAD Models (Proc. IEEE Visualization '97, pp.363-370, October 1997); and Gueziec et al., Converting Sets of Polygonsto Manifold Surfaces by Cutting and Stitching, (Proc. IEEE Visualization1998, pp. 383-390, October 1998). Each of these references isincorporated into this Detailed Description in their entireties.

As can be understood from FIGS. 44G and 44H, the integrated jig model748 y may include several features based on the surgeon's needs. Forexample, the jig model 748 y may include a slot feature 30 for receivingand guiding a bone saw and drill holes 32 for receiving and guiding bonedrill bits. As can be understood from FIGS. 44D and 44E, to providesufficient structural integrity to allow the resulting tibia jig 2B tonot buckle or deform during the arthroplasty procedure and to adequatelysupport and guide the bone saw and drill bits, the gap between themodels 232M, 40 may have the following offsets V₁, V₂, and V₃.

As can be understood from FIGS. 44D-44H, in one embodiment, thickness V₁extends along the length of the posterior drill holes 32P between themodels 632M, 40 and is for supporting and guiding a bone drill receivedtherein during the arthroplasty procedure. Thickness V₁ may be at leastapproximately four millimeters or at least approximately fivemillimeters thick. The diameter of the posterior drill holes 32P may beconfigured to receive a cutting tool of at least one-third inches.

Thickness V₂ extends is the thickness of the jig foots 800 y, 802 ybetween the inner and exterior surfaces 40, 632M. The thickness providesadequate structural strength for jig foots 800 y, 802 y, to resistbuckling and deforming of the jig to manufacture and use. Thickness V₂may be at least approximately five millimeters or at least eightmillimeters thick.

Thickness V₃ extends along the length of a saw slot 30 between themodels 632M, 40 and is for supporting and guiding a bone saw receivedtherein during the arthroplasty procedure. Thickness V₃ may be at leastapproximately 10 mm or at least 15 mm thick.

In addition to providing sufficiently long surfaces for guiding drillbits or saws received therein, the various thicknesses V₁, V₂, V₃ arestructurally designed to enable the tibia jig 2B to bear vigorous tibiacutting, drilling and reaming procedures during the TKR surgery.

As indicated in FIGS. 44G and 44H, the exterior portion or side 106 ofthe integrated jig model 748 y may include: feature or jig foot 800 ythat extends over and matches the patient's medial portion of the tibiaplateau; feature or jig foot 802 y that extends over and matches thepatient's lateral portion of the tibia plateau; projection 804 y thatextends downward from the upper exterior surface 632 y of the tibia jig2B; and a flat portion of the exterior surface 632 y that provides ablanked labeling area for listing information regarding the patient,surgeon or/and the surgical procedure. Also, as discussed above, theintegrated jig model 748 y may include the saw cut slot 30 and the drillholes 32. The inner portion or side 104 of the jig model 748 y (and theresulting tibia jig 2B) is the tibia surface model 40, which willmatingly receive the arthroplasty target area 42 of the patient's tibia20 during the arthroplasty procedure.

As can be understood by referring to [block 105] of FIG. 1B and FIGS.43A-43C, in one embodiment when cumulating the image scans 16 togenerate the one or the other of the models 40, 22, the models 40, 22are referenced to point P, which may be a single point or a series ofpoints, etc. to reference and orient the models 40, 22 relative to themodels 22, 28 discussed with respect to FIG. 1C and utilized for POP.Any changes reflected in the models 22, 28 with respect to point P(e.g., point P becoming point P′) on account of the POP is reflected inthe point P associated with the models 40, 22 (see [block 135] of FIG.1D). Thus, as can be understood from [block 140] of FIG. 1D and FIGS.44C-44E, when the jig blank exterior surface model 632M is combined withthe surface model 40 (or a surface model developed from the arthriticmodel 22) to create the jig model 746 y, the jig model 746 y isreferenced and oriented relative to point P′ and is generally equivalentto the “jig data” 46 discussed with respect to [block 145] of FIG. 1E.

Because the jig model 746 y is properly referenced and oriented relativeto point P′, the “saw cut and drill hole data” 44 discussed with respectto [block 125] of FIG. 1E can be properly integrated into the jig model746 y to arrive at the integrated jig model 748 y depicted in FIGS.44G-44H. The integrated jig model 748 y includes the saw cuts 30, drillholes 32 and the surface model 40. Thus, the integrated jig model 748 yis generally equivalent to the “integrated jig data” 48 discussed withrespect to [block 150] of FIG. 1E.

As can be understood from FIG. 44I, which illustrates a perspective viewof the integrated jig model 748 y mating with the “arthritic model” 22,the interior surface 40 of the jig model 748 y matingly receives thearthroplasty target area 42 of the tibia upper end 604 y such that thejig model 748 y is indexed to mate with the area 42. Because of thereferencing and orientation of the various models relative to the pointsP, P′ throughout the procedure, the saw cut slot 30 and drill holes 32are properly oriented to result in saw cuts and drill holes that allow aresulting tibia jig 2B to restore a patient's joint to a pre-degeneratedcondition.

As indicated in FIG. 44I, the integrated jig model 748 y may include ajig body 850 y, a medial tibia plateau covering projection 852 y, alateral tibia plateau covering projection 854 y, a lower portion 856 yextending form the body 850 y, posterior drill holes 32P, anterior drillholes 45N, a saw slot 30 and an upper flat portion 857 y for receivingthereon patient, surgery and physician data. The projections 852 y, 854y extend over their respective medial and lateral tibia plateauportions. The projections 852 y, 854 y, 856 y, 857 y extend integrallyfrom the jig body 850 y.

As can be understood from [blocks 155-165] of FIG. 1E, the integratedjig 748 y or, more specifically, the integrated jig data 48 can be sentto the CNC machine 10 to machine the tibia jig 2B from the selected jigblank 50B. For example, the integrated jig data 48 may be used toproduce a production file that provides automated jig fabricationinstructions to a rapid production machine 10, as described in thevarious Park patent applications referenced above. The rapid productionmachine 10 then fabricates the patient-specific arthroplasty tibia jig2B from the tibia jig blank 50B according to the instructions.

The resulting tibia jig 2B may have the features of the integrated jigmodel 748 y. Thus, as can be understood from FIG. 44I, the resultingtibia jig 2B may have the slot 30 and the drilling holes 32 formed onthe projections 852 y, 854 y, 856 y, 857 y, depending on the needs ofthe surgeon. The drilling holes 32 are configured to prevent thepossible IR/ER (internal/external) rotational axis misalignment betweenthe tibia cutting jig 2B and the patient's damaged joint surface duringthe proximal tibia cut portion of the TKR procedure. The slot 30 isconfigured to accept a cutting instrument, such as a reciprocating slawblade for transversely cutting during the proximal tibia cut portion ofthe TKR.

i. Overestimation Process

As mentioned above in Subsection a of this Detailed Description, certainregions of the 3D surface models 40 may be a more accuraterepresentation of the actual patient bone surface than other regionsand/or may be more readily machined. For example, because of limitationsin the medical imaging process (e.g., having to rely on a finite numberof image slices 16 as opposed to an infinite number of image slices,volume averaging issues, and issues presented by irregular contours dueto the presence of osteophytes, fat tissue, broken cartilage, etc.), the3D surface models 40 in certain regions may not be an accuraterepresentation of the corresponding actual bone surfaces of thearthroplasty target areas. As a result, a bone mating surface of anactual jig 2 based upon such less accurate data may end up having aninterfering fit as opposed to a mating fit with the arthroplasty targetarea of the actual bone surfaces.

With respect to machining, the size of the tooling used to machine thebone mating surface of the actual jig may exceed the size of certainfeatures in the 3D surface models 40. As a result, the CNC machine maynot be able to accurately machine the bone mating surface of the actualjig to match the 3D surface models.

To address these issues presented by the imaging and machininglimitations, the 3D surface models 40, or more specifically, the contourlines 210 y, 210 y′ used to generate the 3D surface models, may besubjected to the overestimation process described below. The result ofthe overestimation process is an actual jig with: (1) bone matingsurfaces that matingly receive and contact certain regions of the actualbone surface of the arthroplasty target region, wherein the certainregions correspond to regions of the actual bone surface that can beaccurately and reliably 3D computer modeled and actually machined; and(2) bone-facing surfaces of the jig (i.e., those surfaces of the jigthat face the bone when the bone mating surfaces of the jig matinglyreceive and contact the bone surfaces of the arthroplasty target region)that avoid contact with certain other regions of the actual bone surfaceof the arthroplasty target region, wherein the certain other regionscorrespond to regions of the actual bone surface that are less likely tobe accurately and reliably 3D computer modeled and/or less likely to beactually machined.

In creating bone-facing surfaces of the jig that correspond to bonesurface regions that are less likely to be accurately 3D modeled and/oractually machined, the overestimation process overestimates or moves thecontour lines 210 y away or outward from the bone area of the imageslice 16 such that the CNC machine will be caused to over-machine alongthe overestimated contour line. This outward displacement of the contourline 210 y results in the jig's bone-facing surface corresponding to theoverestimated contour line being spaced apart from the correspondingactual bone surface of the arthroplasty target region when the jig'sbone mating surface matingly receives and contacts the arthroplastytarget region.

Due to the overestimation process, in one embodiment, the contactbetween the jig's bone mating surface and the bone surface of thearthroplasty target region is limited to those regions of thearthroplasty target region that can be accurately and reliably 3Dcomputer modeled and actually machined. All other bone-facing surfacesof the jig may be the result of the overestimation process such thatthese other bone-facing surfaces are spaced apart from, and do notcontact, their corresponding regions of the bone surface of thearthroplasty target region, as these bone regions correspond to regionsthat are less likely to be accurately 3D computer modeled and/or lesslikely to be actually machined. The result of the overestimatedbone-facing surfaces of the jig 2 is a jig that is more likely toaccurately and reliably matingly receive the arthroplasty target regionduring an arthroplasty procedure.

Example overestimation processes are provided below in the context ofgenerating bone-facing surfaces for a femur jig and a tibia jig, whereinsome of the bone-facing surfaces are bone mating surfaces and otherbone-facing surfaces are the result of overestimation. While thefollowing examples are provided in the context of jigs for kneearthroplasty, the overestimation process should not be considered asbeing limited to the knee context. Instead, the overestimation conceptsdisclosed herein should be considered to be applicable to all types oforthopedic surgeries by those skilled in the art, including thosesurgeries for other types of bone-to-bone interfaces such as ankle, hip,wrist, elbow, shoulder, toe, finger and other types of joints,vertebrae-to-vertebrae interfaces, vertebrae-to-hip structureinterfaces, vertebrae-to-skull interfaces, etc.

1. Overestimating the 3D Femur Surface Models

As described above with regard to block 140 of FIG. 1D, the “jig data”46 is used to produce a jigs having bone mating surfaces customized tomatingly receive the target areas 42 of the respective bones of thepatent's joint. Data for the target areas 42 may be based, at least inpart, on the 3D computer generated surface models 40 of the patient'sjoint bones. Furthermore, as described above with regard to FIG. 1A and[blocks 100-105] of FIG. 1B, these 3D computer generated surface models40 may be based on the plurality of 2D scan image slices 16 taken fromthe imaging machine 8 and, more precisely, from the contour linesderived from those 2D scan image slices via image segmentation processesknown in the art or, alternatively, as disclosed in U.S. ProvisionalPatent Application 61/126,102, which was filed Apr. 30, 2008 and isincorporated by reference herein in its entirety.

Each scan image slice 16 represents a thin slice of the desired bones.FIG. 45A illustrates the distal axial view of the 3D model of thepatient's femur shown in FIG. 42A with the contour lines 2301 of theimage slices shown and spaced apart by the thickness D_(T) of theslices. FIG. 45B represents a coronal view of a 3D model of thepatient's femur with the contour lines 2301 of the image slices shownand spaced apart by the thickness D_(T) of the slices.

The slices shown in FIGS. 45A-B have contour lines 2301 similar to theopen and closed loop contour line segments 210 y, 210 y′ depicted inFIGS. 41B and 41E. The contour lines 2301 of each respective image slice16 are compiled together to form the 3D model of the patient's femur.The overall resolution or preciseness of the 3D models 40 (shown inFIGS. 41C and 41F) resulting from compiling together the contour linesof each of these slices (shown in [block 1010]) may be impacted by thethickness D_(T) of the slices shown in FIGS. 45A-B. Specifically, thegreater the thickness D_(T) of the slices, the lower theresolution/preciseness of the resulting 3D models, and the smaller thethickness D_(T) of the slices, the higher the resolution/preciseness ofthe resulting 3D models.

As the resolution/preciseness of the 3D models increases, more accuratecustomized arthroplasty jigs 2 may be generated. Thus, the generalimpetus is to have thinner slices rather than thicker slices. However,depending upon the imaging technology used, the feasible thickness D_(T)of the image slices may vary and may be limited due a variety ofreasons. For example, an imaging thickness D_(T) that is sufficientlyprecise to provide the desired imaging resolution may also need to bebalanced with an imaging duration that is sufficiently brief to allow apatient to remain still for the entire imaging duration.

In embodiments utilizing MRI technology, the range of slice thicknessD_(T) may be from approximately 0.8 mm to approximately 5 mm. MRI slicethicknesses D_(T) below this range may be unfeasible because they haveassociated imaging durations that are too long for most patients toremain still. Also, MRI slice thicknesses D_(T) below this range may beunfeasible because they may result in higher levels of noise with regardto actual signals present, residuals left between slices, and volumeaveraging limitations of the MRI machine. MRI slice thicknesses abovethis range may not provide sufficient image resolution/preciseness. Inone embodiment, the MRI slice thicknesses D_(T) is approximately 2 mm.

While embodiments utilizing CT technology may have a range of slicethicknesses D_(T) from approximately 0.3 mm to approximately 5 mm, CTimaging may not capture the cartilage present in the patient's joints togenerate the arthritic models mentioned above.

Regardless of the imaging technology used and the resultingresolution/preciseness of the 3D models, the CNC machine 10 may beincapable of producing the customized arthroplasty jigs 2 due tomechanical limitations, especially where irregularities in the bonesurface are present. This, for example, may result where a milling toolbit has dimensions that exceed those of the feature to be milled.

FIG. 45C illustrates an example sagittal view of compiled contour linesof successive sagittal 2D MRI images based on the slices shown in FIGS.45A-B with a slice thickness D_(T) of 2 mm. As can be understood fromFIGS. 45A-23, the contour lines shown begin on the medial side of theknee at the image slice corresponding to contour line 2310 and concludeon the lateral side of the knee at the image slice corresponding tocontour line 2330. Thus, in one embodiment, contour lines 2310 and 2330represent the contour lines of the first and last images slices taken ofthe femur, with the other contour lines between contour lines 2310, 2330representing the contour lines of the intermediate image slices taken ofthe femur. Each of the contour lines is unique is size and shape, may beeither open-loop or closed-loop, and corresponds to a unique image slice16.

FIG. 45D illustrates an example contour line 2400 of one of the contourlines depicted in FIGS. 45A-23, wherein the contour line 2400 isdepicted in a sagittal view and is associated with an image slice 16 ofthe femoral condyle. As shown, the contour line 2400 includes aplurality of surface coordinate points (e.g., h−n, . . . , h−3, h−2,h−1, h, h+1, h+2, h+3, . . . , h+n; j−n, . . . , j−3, j−2, j−1, j, j+1,j+2, j+3, . . . , j+n; k−n, . . . , k−3, k−2, k−1, k, k+1, k+2, k+3, . .. , k+n; and i−n, . . . , i−3, i−2, i−1, i, i+1, i+2, i+3, . . . , i+n).The contour line and associated points may be generated by imagingtechnology, for example, via an image segmentation process that mayemploy, for example, a shape recognition process and/or a pixelintensity recognition process. In one embodiment, the contour line 2400may represent the boundary line along the cortical-cancellous bone edge.In one embodiment, the boundary line may represent the outer boundaryline of the cartilage surface.

Each of the surface contour points in the plurality may be separated bya distance “d”. In one embodiment, distance “d” may be a function of theminimum imaging resolution. In some embodiments, distance “d” may befunction of, or associated with, the size of the milling tool used tomanufacture the jig. For example, the distance “d” may be set to beapproximately 10 times smaller than the diameter of the milling tool. Inother words, the distance “d” may be set to be approximately 1/10^(th)or less of the diameter of the milling tool. In other embodiments, thedistance “d” may be in the range of between approximately one half ofthe diameter of the milling tool to approximately 1/100^(th) or less ofthe diameter of the milling tool.

Depending on the embodiment, the separation distanced may be eitheruniform along the contour line 2400, or may be non-uniform. For example,in some embodiments, areas of bone irregularities may have points thatare closer together than areas where no irregularities are present. Inone embodiment, the points shown along the example contour line 2400 mayhave a separation distance d of approximately 2 mm. In otherembodiments, distance d may be in the range of approximately 0.8 mm toapproximately 5 mm.

The bone surface of the example contour line 2400 includes a regularregion 2402A on the distal-posterior portion of the contour line 2400 aswell as an irregular region 2402B of the same. The contour line 2400also includes irregular regions 2402C-D on the distal anddistal-anterior portions, respectively. The irregular regions 2402B-Dmay be due to a variety of patient specific factors. For example,irregular region 2402B illustrates a type of bone irregularity, referredto as an “osteophyte”, where a bony outgrowth has occurred in thefemoral condyle. Osteophytes may be present in patients that haveundergone trauma to the bone or who have experienced degenerative jointdisease.

The irregular regions 2402C-D illustrate areas of the femoral condylethat have experienced cartilage damage and appear as notches in thecontour line 2400. Regardless of the cause of the irregularity, thepresence of irregularities in the contour line 2400 may adversely impactthe ability to generate a mating surface in the actual arthroplasty jigthat accurately and reliably mates with the corresponding bone surfaceof the patient during the arthroplasty procedure. This may be the resultof the imaging impreciseness in the vicinity of the contour irregularregions 2402B-D or because the contour irregular regions 2402B-Drepresent surface contours that are too small for the tooling of the CNCmachine 10 to generate. To account for contour line regions associatedwith imaging impreciseness and/or features too small to be milled viathe tooling of the CNC machine, in some embodiments, such contour lineregions may be identified and corrected or adjusted via theoverestimation process prior to being compiled to form the 3D models 40.

FIG. 45E represents an example overestimation algorithm 2500 that may beused to identify and adjust for irregular regions 2402B-D when formingthe 3D models 40. In block 2502, medical imaging may be performed on thedamaged bone at desired slice thicknesses D_(T), which in someembodiments may be equal to those slice thicknesses D_(T) mentionedabove with regard to FIGS. 45A-45B. For example, MRI and/or CT scans maybe performed at predetermined thicknesses D_(T) as shown in FIGS. 45A-B.In some embodiments, the desired thickness D_(T) used in block 2502 isset at 2 mm or any other thickness D_(T) within the range of thicknessesD_(T) mentioned above.

From this medical imaging, a series of slices 16 may be produced andimage segmentation processes can be used to generate the contour lines210 y, 210 y′, 2301, 2310, 2330, 2400 discussed with respect to FIGS. 2,45A-B, and 24 (see block 2504). Also in block 2504, a plurality ofsurface coordinate points along each contour line segment 2402A-D may beidentified as shown in FIG. 45D with respect to contour line 2400. Forexample, the points in the irregular region corresponding to contourline segment 2402B may be identified and indexed as i−n, . . . , i−1, i,i+1, i+2, i+3, . . . , i+n.

With the surface coordinate points along the contour 2400 defined, ananalysis may be performed on two or more of the points (e.g., i and i+1)to determine if an irregularity exists in the contour line segment perblock 2506.

FIG. 45F depicts implementing an example analysis scheme (according toblock 2506) on the irregular contour line region 2402B of FIG. 45D. Asshown, the analysis may include constructing one or more tangent lines(labeled as t_(i−1), t_(i), t_(i+1), t_(i+2), t_(i+3), t_(i+4), etc.),corresponding to the points in the irregular region 2402B. The analysisof block 2506 may further include calculating differences between theangles formed by one or more of the tangent lines. For example, thedifference between the angles formed by the tangent lines t_(i) andt_(i+1) may be defined as w_(i), where

$w_{i} = {{\cos^{- 1}\left( \frac{t_{i + 1} \cdot t_{i}}{{t_{i + 1}}{t_{i}}} \right)}.}$In some embodiments, the operations of block 2506 may be performedrepetitively on each point within the contour segment.

The operations of block 2506 may be calculated on subsequent points(e.g., between t_(i) and t_(i+1)) in some embodiments, and onnon-subsequent points in other embodiments (e.g., t_(i+2) and t_(i+4)).

The angular difference w, may indicate whether portions of the contourline segment are too eccentric for use in constructing the 3D models 40.In block 2508, the angular difference w_(i) may be compared to apredetermined angular criterion w_(c). The angular criterion w_(c) maybe determined based on several factors, including the physicaldimensions and characteristics of the CNC machine 10. In someembodiments, the predetermined angular criterion w_(c) is set atapproximately 5 degrees. In other embodiments, the predetermined angularcriterion w_(c) is set at between approximately 5 degrees andapproximately 20 degrees.

For the sake of discussing the example irregular region 2402B shown inFIG. 45F, the angular criterion w_(c) is set to 5 degrees in oneembodiment. The angular differences between tangent lines associatedwith adjacent points i−2, i−1, i, i+1, i+2 are within the predeterminedangular criterion w_(c) of 5 degrees, but the differences betweentangent lines associated with adjacent points i+2 and i+3 and adjacentpoints i+3 and i+4 exceeds the predetermined angular criterion w_(c) of5 degrees and therefore indicates an irregular region of the contourline. The difference between tangent lines associated with adjacentpoints, such as i+5 and i+6, may indicate similar irregular regions. Asmentioned above, these irregularities may result from conditions of thepatient's bone such as arthritis or osteoarthritis and generally resultin a contour line segment being unsuitable for using when forming the 3Dmodels 40. Accordingly, if the comparison from block 2508 indicates thatthe angular difference w_(i) is greater than the predetermined criterionw_(c), then the data associated with the irregular contour line segmentmay be modified by overestimating (e.g., adjusting the irregular contourline segment outward or away from the bone portion of the image slice16) as discussed in greater detail below with respect to FIG. 45G (seeblock 2510).

FIG. 45G depicts the irregular region 2402B from FIG. 45F including aproposed area of overestimation, wherein an overestimation procedurecreates an adjusted contour line 2702 and positionally deviates theadjusted contour line 2702 from the original surface profile contourline 2402B. In the event that the comparison performed in block 2508indicates that the angular differences between any of the points ithrough i+14 exceed the predetermined angular criterion w_(c), then thecontour line segment may be overestimated between these points as shownby the dashed line 2702. As can be understood from a comparison ofcontour line 2402B to the overestimated or adjusted line 2702, theadjusted line 2702 is adjusted or moved outward or away from thelocation of the contour line 2402B by an offset distance. Depending onthe embodiment, the offset distance between the contour line 2402B andthe adjusted line 2702 may range between a few millimeters to a fewcentimeters. This overestimation may be built into the data used toconstruct 3D surface models 40 and result in a gap between therespective region of the bone mating surface of the jig 2 and thecorresponding portion of the patient's bone surface, thereby avoidingcontact between these respective areas of the jig and bone surface. Theother areas, such as i−1, i−2, i−3, i+15, i+16, i+17, and i+18, need notbe overestimated, per block 2510, because the differences between theirtangent lines fall within the angular difference criterion w_(c). Theseareas may be designated as potential target areas that may later be usedas the 3D surface models 40 if other angular criteria (described below)are satisfied.

By building overestimation data into the 3D surface models 40,deliberate spaces may be created in regions of the custom arthroplastyjig 2 corresponding to irregularities in the patient's bone, where it isoften difficult to predict the size and shape of these irregularitiesfrom 2D MRI or where it is difficult to accurately machine the contourline into the jig's bone mating surface because of the largeness of themilling tool relative to the changes in contour. Thus, the jig 2 mayinclude one or more deliberate spaces to accommodate theseirregularities or inability to machine. Without these deliberate spaces,the jig 2 may be potentially misaligned during the TKR surgery and mayreduce the chances of the surgery's success.

The image generation, analysis and overestimation of blocks 2506, 2508and 2510 may be performed on the other irregularities shown in FIG. 45D.FIG. 45H illustrates the example analysis scheme according to algorithm2500 implemented on the irregular region 2402C where an irregularsurface of the condylar contour is observed. Akin to the analysis ofirregular region 2402B, the analysis may include constructing one ormore tangent lines (labeled as t_(j−1), t_(j), t_(j+1), t_(j+2),t_(j+3), etc.), corresponding to the points in the irregular region2402C. The analysis of block 2506 may further include calculatingdifferences between the angles formed by one or more of the tangentlines, defined as w_(j), where

$w_{j} = {\cos^{- 1}\left( \frac{t_{j + 1} \cdot t_{j}}{{t_{j + 1}}{t_{j}}} \right)}$between subsequent points t_(j) and t_(j+1). Other embodiments includeanalysis between non-subsequent points (e.g., t_(j+2) and t_(j+4)).

Akin to the analysis of irregular region 2402B, the angular differencew_(j) may indicate whether portions of the contour line segment in theirregular region 2402C are too eccentric for use in constructing the 3Dmodels 40. In block 2508, the angular difference w_(j) may be comparedto a predetermined angular criterion w_(c). If the angular criterionw_(c) is set to 5 degrees, the angular differences between adjacenttangent lines associated with j−6, j−5, j−4, j−3, j−2 and j−1 are withinthe predetermined angular criterion w_(c). The difference between j−1,j, and j+1, however, may exceed the predetermined angular criterionw_(c) of 5 degrees and therefore may indicate an irregular region of thecontour line 2400. In a similar fashion, the angular criterion w_(c) forangular differences between tangent lines associated with subsequentpoints j−6, j−7, and j−8 may indicate similar irregular regions.

As mentioned above, these irregularities may result from conditions inthe patient's bone such as arthritis or osteoarthritis and generallyresult in a contour line segment being unsuitable for using when formingthe 3D models 40. Accordingly, if the comparison from block 2508indicates that the angular difference w_(i) is greater than thepredetermined criterion w_(c), such as the case at points j−1, j, andj+1 as well as j−6, j−7, and j−8, then the data used in forming 3Dmodels 40 may be adjusted by the overestimating process prior to beingused in forming the 3D models 40.

FIG. 45I depicts the irregular region 2402C from FIG. 45H including aproposed area of overestimation indicated by the dashed line areas2902A-B, wherein the dashed line areas 2902A-B are deviated from theoriginal cortical-cancellous boundary or contour line 2402C. Since thecomparison performed in block 2508 indicates that the angular differencew_(j) is greater than the predetermined criterion w_(c) at points j−1,j, and j+1 as well as at points j−6, j−7, and j−8, overestimation isperformed at these points (labeled as regions 2902A-B respectively). Insome embodiments to allow for an adequate transition from thenon-overestimate regions to the overestimated regions in view of thediameter of the tool to be used, the overestimation may includeadditional points to either side of the points falling outside of thepredetermined criterion w_(c) (i.e., points j−1, j, and j+1 as well asat points j−6, j−7, and j−8). Thus, the overestimation in region 2902Amay extend from j−2 through j+2, and the overestimation in region 2902Bmay extend from j−10 through j−5. Furthermore, since the comparisonperformed in block 2508 indicates that the angular difference w_(j) isless than the predetermined criterion w_(c) at points j−6, j−5, j−4,j−3, and j−2, (labeled as region 2902C) these points j−5, j−4, and j−3(adjusting for the addition of points j−6 and j−2 to the regions2902A-B) may be used in constructing the 3D models 40 as long as othercriteria (described below in the context of blocks 2514-2520) are met.

A tool 2904 may be used to form the surface of the jig's bone matingsurface from the 3D models 40 formed from the compiled contour lines,some of which may have been modified via the overestimation process. Thetool 2904 may be part of the CNC machine 10 or any other type ofmachining or manufacturing device having any type of tool or device forforming a surface in a jig blank. Regardless of the type of the deviceused to mill or form the jigs 2, the tool 2904 may have certainattributes associated with jig machining process that are taken intoaccount when performing the overestimating per block 2510. Theassociated attributes may include the accessible space for the machiningtools to reach and machine the jig's bone mating surface. Examples ofsuch attributes may include the collar diameter of the drilling cutterdevice, the allowable angle the drilling device can make with thesurface to be drilled (e.g., 45 degrees±10%), and/or the overall lengthof the drilling cutter head.

For example, if the minimum diameter of the overestimated regions2902A-B is larger than the diameter D₁ of the tool 2904, thenoverestimation of block 2510 may not need to account for the dimensionsof the tool 2904, except to provide adequate transitions leading to theoverestimated regions as illustrated above by the addition of a singleor few points (e.g., points j−2, j+2, j−5, and j−10) to either side ofthe points outside predetermined criterion w_(c).

If, on the other hand, the tool 2904 has a larger diameter D₂ as shownin the example implementation of FIG. 45J, then the overestimationperformed in block 2510 may include accounting for this larger tool sizein its overestimation. To determine if the overestimation needs to beadjusted to accommodate the larger diameter D₂, a first measurement ofthe minimum diameter of curvatures 2902A′ and 2902B′ for regions 2902A-Bmay be made. In addition, a second measurement of half of the distanceassociated with region 2902C plus the minimum diameter of curvatures2902A′ and 2902B′ for regions 2902A-B may be made. If both the first andsecond measurements are less than the diameter D₂, then the amount ofoverestimation implemented in block 2510 may be set such that theminimum curvatures of regions 2902A-B, respectively, are greater than orequal to D₂ and are increased to 2902A″ and 2902B″, respectively.Logically, this example curvature requirement may be expressed as: ifdiameter_(MIN)(2902A OR 2902B)<D₂ AND (diameter_(MIN)(2902A OR2902B)+(2902C)/2)<D₂, then overestimate so that diameter_(MIN)(2902Aand/or 2902B) D₂. Also, in the event that the overestimation needs toaccount for the tool diameter D₂, one or more additional points, overwhat would normally be required absent the need to account for tooldiameter, may be included such that the regions 2902A-B respectivelyextend through points j−4 through j+2 and j−12 through j−4. Thecurvatures 2902A′ and 2902B′ for the respective regions 2902A-B may befurther adjusted outward (as indicated by the arrows in FIG. 45J) to therespective diameter-accounted curvatures 2902A″ and 2902B″ to define thepotential jig mating surface for the 3D models 40. Thus, regions 2902A-Bmay increase in size to accommodate the diameter D₂ of the tool 2904 bysacrificing the area of region 2902C. It should be noted that, if addinga one or more points on either side of an overestimation region 2902A,2902B in the course of accounting for tool diameter does not result in asmooth transition into the resulting curvature 2902A″, 2902B″, thenstill further points can be added to the overestimation region until asmooth transition is achieved.

FIG. 45K shows an example implementation of the tool 2904 having an evenlarger diameter D₃ than what is shown in FIGS. 45I-J. In this scenario,if diameter_(MIN)(2902A OR 2902B)<D₃ AND (diameter_(MIN)(2902A OR2902B)+(2902C)/2)<D₃, then overestimate so thatdiameter_(MIN)(2902A-C)<D₃. As illustrated by the arrows, all threeregions 2902A-C may need to be overestimated if the size of tooldiameter is large enough, sacrificing the entirety of region 2902C tothe overestimation associated with regions 2902A-B. Thus, the initialoverestimation curvatures 2902A′ and 2902B′ end up being a singlecurvature 2902A-C″ encompassing all of regions 2902A-C. Of course,additional points can be added as needed to either side ofoverestimation region 2902A-C to provide a smooth transition into theresulting curvature 2902A-C″.

With the curves overestimated to account for factors related to the tool2904, the resulting overestimated surface profile or contour may besaved for generating the 3D model 40 as long as other criteria(described below in the context of block 2514-2520) are met.

Referring briefly back to FIG. 45D, the analysis and overestimation ofalgorithm 2500 may be performed on the irregular region 2402D, where theboundary between the cortical and cancellous bone in the femoral condyleis irregular and may not be clearly identified by the imaging slices.FIG. 45L illustrates the example overestimation scheme on the irregularregion 2402D according to block 2510. As shown in FIG. 45L, theirregular region 2402D extends between points h+1 to h+10. The tangentlines (not shown in FIG. 45L) of every two adjacent coordinate pointsshown have an angular difference greater than w_(c), and therefore,overestimation may be performed as shown by the dashed line 3002 betweenpoints h−2 to h+13.

FIG. 45M shows a similar analysis of the regular region 2402A (from FIG.45D). As was the case with the irregular regions 2402B-D, points alongthe contour line k-1 through k+4 may be identified and then tangentlines (labeled as t_(k−1), t_(k), t_(k+1), t_(k+2), t_(k+3), etc.) maybe constructed per block 2506. Per block 2508, comparing the angulardifferences w_(k) between these tangent lines using the formula

$w_{k} = {\cos^{- 1}\left( \frac{t_{k + 1} \cdot t_{k}}{{t_{k + 1}}{t_{k}}} \right)}$shows that they are within the angular criterion w_(c), which in thisexample is 5 degrees. Thus, the points shown in FIG. 45M may be savedand used as a potential surface profile for the mating surface of thefemoral jig if the surface variations between these points and points oncontour lines of adjacent slices are not too extreme. That is, if theangular differences associated with a contour line of a particular slicefall within the angular criterion w_(c), and the points are used as apotential jig surface, then surface variation between contour lines ofadjacent slices may be checked in block 2514. This approach may help toidentify certain areas where no cartilage damage or osteophyte isobserved in the imaging, yet there is a need to overestimate because thesurface variation, between the adjacent slices shown in FIGS. 45A-B, maybe too great to be used as an accurate representation of the actual bonesurface to be a potential femoral jig surface. Example areas fallingwithin this category for the femoral condyle include, the area ofanterior condylar portion close to the trochlear groove and the area ofdistal condylar portion close to the intercondylar notch to name a fewexamples.

FIG. 45N is a diagrammatic sagittal-coronal-distal isometric view ofthree contour lines 210 y of three adjacent image slices 16 depictingangular relationships that may be used to determine whether portions ofthe one or more contour lines may be employed to generate 3D computermodels 40. As mentioned above, despite contour line segments and theirassociated coordinate points meeting the angular criterion w_(c) so asto not require overestimation as discussed with respect to blocks 2508and 2510, such contour line segments and associated coordinate pointsmay still require overestimation if the surface variations betweensurface contour lines 210 y of adjacent imaging slices 16 is excessive.Excessive surface variation may result in volume averaging error in theregions of the 3D computer generated models corresponding to theexcessive surface variation. Jig mating surfaces based on regions of the3D computer generated models that are the result of volume averagingerror are may have difficulty accurately matingly receiving theassociated bone surfaces of the arthroplasty target region.

Such excessiveness is typically the result of variations in thepatient's knee features. For example, in the majority of cases, the areaof the anterior condylar portion close to the trochlear groove isobserved as a smooth depression. However, in other patients, a sharpedge is present in place of the smooth depression. Because of thevariation in anatomy between various patients for these varying surfaceareas and/or other varying surface areas (e.g., the area of distalcondylar portion close to the intercondylar notch), these varyingsurface areas may be generally excluded from being a potential contourline for generating a 3D model 40. In other words, such varying surfaceareas may be subjected to an overestimation process as described below.

The three contour line segments are respectively labeled in FIG. 45N asthe m^(th), m^(th+1), m^(th+2) contour line segments corresponding tothree consecutive image slices 16 spaced apart from each other by slicethickness D_(T). Each contour line includes surface contour points A-C,A′-C′ and A″-C″ that are saved for use in the potential jig surfaceprofile because, for example, the points fall within the angularcriteria discussed with respect to blocks 2506 and 2508. The points A-C,A′-C′ and A″-C″ now may be used to determine if the slice-to-slicesurface variation exceeds a predetermined threshold. For example, on them^(th) contour line in FIG. 45N, points A, B, and C may have beenidentified in blocks 2506 and 2508 as defining potential jig matingsurfaces. Similarly, in the m^(th+1) contour line in FIG. 45N, pointsA′, B′, and C′ may have been identified in blocks 2506 and 2508 asdefining potential jig mating surfaces. Likewise, in the m^(th+2)contour line in FIG. 45N, points A″, B″, and C″ may have been identifiedin blocks 2506 and 2508 as defining potential jig mating surfaces.

Because each patient's bone anatomy may be unique, changes in surfacecontour between corresponding points on contour lines of adjacent slices(i.e., from A-A′, A′-A″, B-B′, B′-B″, C-C′, or C′-C″) may be toosignificant for use as potential jig surfaces, resulting in volumeaveraging errors that may lead to surface inaccuracies for the 3Dcomputer models. As will be described in detail below with respect tothe example bone contour lines depicted in FIG. 45N, the bone surfacedefined by points A-A′-A″ may provide a potential jig mating surface,the bone surface defined by points B-B′-B″ may have too much associatednormal vector angular deviation to be used as potential jig matingsurface, and the bone surface defined by points C-C′-C″ may have toomuch associate angular deviation between corresponding points of contourlines of adjacent image slices to be used as a potential jig matingsurface.

As discussed above with respect to FIG. 45D, a contour line 2400 mayhave a plurality of coordinate points. According to the operation ofblock 2508 of FIG. 45E, the coordinate points may fall into one of twoclassifications, namely, those coordinate points within a potential jigmating area 2402A and those coordinate points within a non-jig matingarea 2402B, 2402C and 2402D. Via the criteria of block 2514 of FIG. 45E,the surface coordinate points of one contour line 2400 in potential jigmating area 2402A may be further investigated by a multi-slice (e.g.,three-slice) check. For example, coordinate point k+1 located withinarea 2402A may be coordinate point A in FIG. 45N. Similarly, coordinatepoints k and k−1 within area 2402A may be coordinate points B and C,respectively. Coordinate points A, A′ and A″ may correspond to eachother, coordinate points B, B′ and B″ may correspond to each other, andcoordinate points C, C′ and C″ may correspond to each other.Corresponding points A′, A″, B′, B″, C′, C″ for respective points A, B,C may be identified via a variety of methods, including the threemethods discussed below with respect to FIGS. 46A-46F.

Block 2514 in FIG. 45E illustrates example comparisons and/ordeterminations that may be made between corresponding points on contourlines of adjacent image slices to determine if surface variation is toogreat for the points and contour line segments to be used in generatingjig mating surfaces. The comparisons and/or determinations may involvetwo facets, which are: (1) determining the angular deviation θ betweencorresponding coordinate points of contour lines of adjacent imageslices; and (2) comparing the angular differences φ of normal vectorsassociated with corresponding coordinate points of contour lines ofadjacent image slices. These two facets of the determination areexplained in turn below, followed by an application of these two facetsof the determination to the contours depicted in FIG. 45N.

As can be understood from FIG. 45N, in one embodiment, the comparisonsof the contour lines with respect to angular deviation θ and angulardifferences cp may take place relative to the contour lines of threeadjacent image slices. In other embodiments, the comparisons of thecontour lines with respect to angular deviation θ and angulardifferences cp may take place relative to the contour lines of two, fouror more adjacent image slices. In other words, depending on theembodiment, the comparison of the contour lines may be accomplished ingroups of two, three, four or more contour lines. In one embodiment, thegroups of contour lines evaluated together may be made up of adjacentcontour lines. In other embodiments, one or more of the contour lines ofa group of contour lines may not be an adjacent contour line (e.g. acontour line falling within a group may be skipped).

Where the image slices 16 are sagittal slices such as those slices 2301,2310 and 2330 depicted in FIGS. 45A-23, in one embodiment as providedbelow with respect to FIG. 45N and then again with respect to FIGS.46A-46B, corresponding coordinate points on contour lines 210 y ofadjacent image slices 16 may be those coordinate points that all existin a single plane that is generally perpendicular to the sagittal imageslices. Thus, as can be understood from FIG. 45N, points A, A′ and A″may all exist in a single plane that is perpendicular to the respectiveimage slices. Line segment AA′ extends between points A and A′, and linesegment A′A″ extends between points A′ and A″. Although the linesegments AA′ and A′A″ may all exist in the same single plane that isperpendicular to the respective image slices, the line segments AA′ andA′A″ may be angularly deviated from each other such that they do notextend along a common line. This angular deviation may be the result ofeach point A, A′ and A″ being located on its respective contour linem^(th), m^(th+1) and m^(th+2) and each contour line having a differentelevation at its respective point relative to the corresponding pointson the adjacent contour lines. This elevation difference between thepoints A, A′ and A″ may be because the bone contour geometric shapechanges from contour line m^(th), m^(th+1), m^(th+2) to contour line.The order of the contour lines m^(th), m^(th+1), m^(th+2) may correspondto the order of the respective image slices, the image slice ordercorresponding to the movement of the MRI scan along the knee. Similarrelationships exist for points B, B′ and B″ and for points C, C′ and C″,resulting in similar line segments BB′, B′B″ and CC′, C′C″,respectively.

Once corresponding coordinate points are identified via the methodalready discussed above and below with respect to FIGS. 45N and 46A-46Bor via any of the methods discussed below with respect to FIGS. 46C-46F,the surface variation between adjacent contour lines may be analyzed by:(1) determining the angular deviation θ between corresponding coordinatepoints of contour lines of adjacent image slices; and (2) comparing theangular differences φ of normal vectors associated with correspondingcoordinate points of contour lines of adjacent image slices.

As can be understood from FIG. 45N and already mentioned above, in oneembodiment, the comparisons of the contour lines with respect to angulardeviation θ and angular differences φ may take place relative to thecontour lines of three adjacent image slices. In other embodiments, thecomparisons of the contour lines with respect to angular deviation θ andangular differences φ may take place relative to the contour lines oftwo, four or more adjacent image slices. In other words, depending onthe embodiment, the comparison of the contour lines may be accomplishedin groups of two, three, four or more contour lines. In one embodiment,the groups of contour lines evaluated together may be made up ofadjacent contour lines. In other embodiments, one or more of the contourlines of a group of contour lines may not be an adjacent contour line(e.g. a contour line falling within a group may be skipped).

As can be understood from FIG. 45N, in one embodiment, the contour linesm^(th), m^(th+1), m^(th+2) may be evaluated as a group of three contourlines, wherein contour line m^(th) is compared to contour lines m^(th)and m^(th+2). Contour line m^(th+1) may then be compared to contourlines m^(th+2) and m^(th+3), and contour line m^(th+2) may then becompared to contour line m^(th+3) and contour line m^(th+4).Alternatively, once contour line m^(th) is compared to contour linesm^(th+1) and m^(th+2), the comparison may begin again with a comparisonof contour line m^(th+2) to contour line m^(th+3) and contour linem^(th+4). Alternatively, once contour line m^(th) is compared to contourlines m^(th+1) and m^(th+2), the comparison may begin again with acomparison of contour line m^(th+4) to contour line m^(th+5) and contourline m^(th+6). Similar orders for comparing the contour lines may beused regardless of whether the contour lines are compared in groups oftwo, four or more.

A discussion will now be given regarding the first facet of the surfacevariation analysis, namely, the determination of the angular deviation θbetween corresponding coordinate points of contour lines of adjacentimage slices per block 2514. FIG. 45O is an example right triangle 3214that may be used for determining the angular deviation θ betweencorresponding coordinate points of contour lines of adjacent imageslices per block 2514. The right triangle 3214 illustrates points A andA′ with the line segment AA′ extending between these two points. Thepoints A and A′ lie on respective contour lines m^(th) and m^(th+1). Theimage slices containing the two contour lines m^(th) and m^(th+1) areseparated by the slice thickness D_(T), which is the perpendiculardistance between the two image slices. Thus, the slice thickness D_(T)can be represented in the right triangle 3214 as the long leg of theright triangle 3214, wherein the line segment AA′ is the hypotenuse ofthe right triangle 3214. The rise or fall distance d_(AA′) between thetwo points A and A′ is a distance perpendicular to the slice thicknessD_(T) and is represented on the right triangle 3214 by the short leg ofthe right triangle 3214. The small angle θ_(AA′) of the right triangle3214 represents the angular deviation θ_(AA′) between the correspondingcoordinate points A and A′ of contour lines m^(th) and m^(th+1) ofadjacent image slices per block 2514. Thus, as can be understood fromthe triangle 3214, the angular deviation θ_(AA′) between thecorresponding coordinate points A and A′ of contour lines m^(th) andm^(th+1) of adjacent image slices may be calculated by any of thefollowing three formulas:

${\theta_{{AA}^{\prime}} = {\tan^{- 1}\left( \frac{d_{{AA}^{\prime}}}{D_{T}} \right)}};{\theta_{{AA}^{\prime}} = {\cos^{- 1}\left( \frac{D_{T}}{{AA}^{\prime}} \right)}};{{{or}\mspace{14mu}\theta_{{AA}^{\prime}}} = {{\sin^{- 1}\left( \frac{d_{{AA}^{\prime}}}{{AA}^{\prime}} \right)}.}}$Ideally if there were no surface variation between points A and A′, thenthe length of line segment AA′ would be equal to the slice thicknessD_(T) and the angular deviation θ_(AA′) between the correspondingcoordinate points A and A′ of contour lines m^(th) and m^(th+1) would bezero.

Determining the angular deviation θ_(AA′) between the correspondingcoordinate points A and A′ in this manner may indicate if the surfacebetween points A and A′ is too steep or varied to be used as a potentialjig mating surface. For example, the angular deviation θ between thecoordinate points may be compared to an angular criterion θ_(c), and thesurface corresponding to the coordinate points may be consideredunsuitable for the creation of the jig's bone mating surfaces where theangular deviation θ between the coordinate points is greater than theangular criterion θ_(c). Stated in the reverse and in the context ofcoordinate points A and A′, the surface corresponding to coordinatepoints A and A′ may be a potential candidate for creation of the jig'sbone mating surfaces if the angular deviation θ_(AA′) is less than theangular criterion θ_(c) (i.e., [θ_(AA′)<θ_(c)]=surface corresponding tocoordinate points A and A′ being a potential candidate for the creationof the jig's bone mating surfaces).

In one embodiment, the angular criterion θ_(c) may be approximately onedegree. However, in some embodiments, the angular criterion θ_(c) may bein the range of approximately one to approximately five degrees. Inother embodiments, the angular criterion θ_(c) may be less than orgreater than these recited values for the angular criterion θ_(c).

As can be understood from FIG. 45P, the example right triangle 3214 ofFIG. 45O can be modified to become another example right triangle 3216and used in determining the angular deviationθ_(A′A″ between corresponding coordinate points A′ and A″ of contour lines m)^(th+1) and m^(th+2) of adjacent image slices per block 2514. Thepreceding three tan⁻¹, sin⁻¹ and cos⁻¹ functions may be modified tomatch the circumstances of the example right triangle 3216 of FIG. 45Pto calculate the respective angular deviation θ_(A′A″). Thus, as can beunderstood from FIG. 45P, the angular deviation θ_(A′A″) between thecorresponding coordinate points A′ and A″ of contour lines m^(th+1) andm^(th+2) of adjacent image slices may be calculated by any of thefollowing three formulas:

${\theta_{A^{\prime}A^{''}} = {\tan^{- 1}\left( \frac{d_{A^{\prime}A^{''}}}{D_{T}} \right)}};{\theta_{{A\;}^{\prime}A^{''}} = {\cos^{- 1}\left( \frac{D_{T}}{A^{\prime}A^{''}} \right)}};{{{or}\mspace{14mu}\theta_{A^{\prime}A^{''}}} = {{\sin^{- 1}\left( \frac{d_{A^{\prime}A^{''}}}{A^{\prime}A^{''}} \right)}.}}$

As can be understood from FIGS. 45Q-45T, the right triangle 3214 of FIG.45O can be similarly modified into the respective example righttriangles 3218, 3220, 3222 and 3224 of FIGS. 45Q-45T, which respectivelywill facilitate the determination of the angular deviations θ_(BB′),θ_(B′B″), θ_(CC′), and θ_(C′C″) between corresponding coordinate pointsB and B′, B′ and B″, C and C′, and C′ and C″, respectively. Thepreceding three tan⁻¹, sin⁻¹ and cos⁻¹ functions may be modified tomatch the circumstances of the respective example right triangles 3218,3220, 3222 and 3224 of FIGS. 45Q-45T to calculate the respective angulardeviations θ_(BB′), θ_(B′B″), θ_(CC′), and θ_(C′C″).

In a manner like that discussed with respect to the angular deviationθ_(AA′) between the corresponding coordinate points A and A′, theangular deviation θ between any of the other pairs of correspondingcoordinate points (i.e., A′ and A″, B and B′, B′ and B″, C and C′, andC′ and C″) may be compared to an angular criterion θ_(c). Thus, wherethe angular deviation θ between corresponding coordinate points exceedsthe angular criterion θ_(c), the surface associated with the coordinatepoints may be considered unsuitable for use in the creation of the jig'sbone mating surfaces. Stated in the reverse, the surface correspondingto the coordinate points may be a potential candidate for creation ofthe jig's bone mating surfaces if the angular deviation θ is less thanthe angular criterion θ_(c) (i.e., [θ<θ_(c)]=surface corresponding tothe coordinate points being a potential candidate for the creation ofthe jig's bone mating surfaces).

In one embodiment, the angular criterion θ_(c) may be approximately onedegree. However, in some embodiments, the angular criterion θ_(c) may bein the range of approximately one to approximately four degrees. Inother embodiments, the angular criterion θ_(c) may be less than orgreater than these recited values for the angular criterion θ_(c).

A discussion will now be given regarding the second facet of the surfacevariation analysis, namely, comparing the angular differences φ ofnormal vectors associated with corresponding coordinate points ofcontour lines of adjacent image slices. As indicated in FIG. 45N, eachcontour line surface coordinate point A, A′, A″, B, B′, B″, C, C′ and C″includes a respective tangent line t_(A), t_(A′), t_(A″), t_(B), t_(B′),t_(B″), t_(C), t_(C′), and t_(C″) that is parallel to the plane in whichthe associated contour line m^(th), m^(th+1) and m^(th+2) resides andtangent to the curvature of the associated contour line m^(th), m^(th+1)and m^(th+2) at the respective coordinate point A, A′, A″, B, B′, B″, C,C′ and C″. A normal vector line NV_(A), NV_(A′), NV_(A″), NV_(B),NV_(B′), NV_(B″), NV_(C), NV_(C′), and NV_(C″) extends from eachrespective coordinate point A, A′, A″, B, B′, B″, C, C′ and C″ and isperpendicular to each respective tangent line t_(A), t_(A′), t_(A″),t_(B), t_(B′), t_(B″), t_(C), t_(C′), and t_(C″). The angulardifferences φ_(A-A′) of normal vectors NV_(A) and NV_(A′) associatedwith respective corresponding coordinate points A and A′ of respectivecontour lines m^(th) and m^(th+1) may be determined with the followingformula:

$\varphi_{A - A^{\prime}} = {{\cos^{- 1}\left( \frac{{NV}_{A} \cdot {NV}_{A^{\prime}}}{{{NV}_{A}}{{NV}_{A^{\prime}}}} \right)}.}$Similarly, the angular differences φ_(A′-A″) of normal vectors NV_(A′)and NV_(A″) associated with respective corresponding coordinate pointsA′ and A″ of respective contour lines m^(th+1) and m^(th+2) may bedetermined with the following formula:

$\varphi_{A^{\prime} - A^{''}} = {{\cos^{- 1}\left( \frac{{NV}_{A^{\prime}} \cdot {NV}_{A^{''}}}{{{NV}_{A^{\prime}}}{{NV}_{A^{''}}}} \right)}.}$

The angular differences φ_(B-B′) of normal vectors NV_(B) and NV_(B′)associated with respective corresponding coordinate points B and B′ ofrespective contour lines m^(th) and m^(th+1) may be determined with thefollowing formula:

$\varphi_{B - B^{\prime}} = {{\cos^{- 1}\left( \frac{{NV}_{B} \cdot {NV}_{B^{\prime}}}{{{NV}_{B}}{{NV}_{B^{\prime}}}} \right)}.}$Similarly, the angular differences φ_(B′-B″) of normal vectors NV_(B′)and NV_(B″) associated with respective corresponding coordinate pointsB′ and B″ of respective contour lines m^(th+1) and m^(th+2) may bedetermined with the following formula:

$\varphi_{B^{\prime} - B^{''}} = {{\cos^{- 1}\left( \frac{{NV}_{B^{\prime}} \cdot {NV}_{B^{''}}}{{{NV}_{B^{\prime}}}{{NV}_{B^{''}}}} \right)}.}$

The angular differences φ_(C-C′) of normal vectors NV_(C) and NV_(C′)associated with respective corresponding coordinate points C and C′ ofrespective contour lines m^(th) and m^(th+1) may be determined with thefollowing formula:

$\varphi_{C - C^{\prime}} = {{\cos^{- 1}\left( \frac{{NV}_{C} \cdot {NV}_{C^{\prime}}}{{{NV}_{C}}{{NV}_{C^{\prime}}}} \right)}.}$Similarly, the angular differences φ_(C′-C″) of normal vectors NV_(C′),and NV_(C″) associated with respective corresponding coordinate pointsC′ and C″ of respective contour lines m^(th+1) and m^(th+2) may bedetermined with the following formula:

$\varphi_{C^{\prime} - C^{''}} = {{\cos^{- 1}\left( \frac{{NV}_{C^{\prime}} \cdot {NV}_{C^{''}}}{{{NV}_{C^{\prime}}}{{NV}_{C^{''}}}} \right)}.}$

Determining in this manner the angular differences φ of normal vectorsassociated with respective corresponding coordinate points of respectivecontour lines may indicate if the surface between the correspondingpoints is too varied to be used as a potential jig mating surface. Forexample, the angular differences φ of normal vectors associated withrespective corresponding coordinate points may be compared to an angularcriterion φ_(c), and the surface associated with the correspondingpoints may be considered unsuitable for use in the creation of the jig'sbone contacting surfaces where values for the angular differences φ aregreater than the angular criterion φ_(c). Stated in the reverse, wherethe angular differences φ of normal vectors associated with respectivecorresponding coordinate points is less than an angular criterion φ_(c),the surface corresponding to the coordinate points may be a potentialcandidate for the creation of the jig's bone mating surfaces (i.e.,φ<φ_(c)=surface corresponding to the coordinate points being a potentialcandidate for the creation of the jig's bone mating surfaces). In oneembodiment, the angular criterion φ_(c) may be approximately twodegrees. In some embodiments, the angular criterion φ_(c) may be in therange of approximately two to approximately six degrees. In otherembodiments, the angular criterion φ_(c) may be greater or less thanthese recited values for the angular criterion φ_(c).

Thus, although one or more coordinate points of a contour line maysatisfy the tangent angle criterion w_(c) of block 2508 as discussedabove with respect to FIGS. 45D and 45F-45M, the coordinate points maystill be inadequate for use in generating the jig's bone contactingsurfaces. This inadequateness may result from the failure of thecoordinate points to meet the criterion of block 2514, namely, thefailure of the angular deviation θ between any of the correspondingcoordinate points to meet the angular deviation criterion θ_(c) and/orthe failure of the angular differences φ of normal vectors associatedwith respective corresponding coordinate points to meet the angulardifferences criterion φ_(c). In some embodiments, when one or morecoordinate points fail to meet both the criterion θ_(c) and φ_(c) ofblock 2508, the contour lines in the locations of those failedcoordinate points may be modified via an overestimation process similarto that discussed above with respect block 2510 and FIGS. 45I-45L.

In other embodiments as reflected in block 2516, when one or morecoordinate points fail to meet both the criterion θ_(c) and φ_(c) ofblock 2508, a determination may be made regarding whether or not theslice thickness D_(T) may be adjusted to a thinner slice thicknessD_(T). Reducing the slice thickness D_(T) per block 2518 may reduce thevariations between adjacent contour lines, making it more likely thatthe criterion θ_(c) and φ_(c) will be satisfied for the coordinatepoints were the entire process started over at block 2502 with a newslice thickness D_(T). If it is determined that modifying the slicethickness D_(T) would not be beneficial (e.g., due to slice thicknessD_(T) already being at a minimum because further reduction in slicethickness D_(T) may generate significant high interferences, residuals,signal-to-noise ratios and unreliable volume-averaging in the pixels),then the contour lines may be subjected to overestimation per block2510.

If the one or more coordinate points of a contour line satisfy thetangent angle criterion w_(c) of block 2508 and both of the angularcriterion θ_(c) and φ_(c) of block 2514, then such one or morecoordinate points may be recorded for the generation of the jig's bonemating surface, as indicated in block 2520 of FIG. 45E. In other words,if the one or more coordinate points of a contour line satisfy thetangent angle criterion w_(c) of block 2508 and both of the angularcriterion θ_(c) and φ_(c) of block 2514, then the surfaces associatedwith such one or more coordinate points may be employed in thegeneration of corresponding bone mating surfaces of the jig, asindicated in block 2520.

An example application of the functions of block 2514 with respect tothe contour lines m^(th), m^(th+1) and m^(th+2) depicted in FIG. 45Nwill now be provided. In this example, it is assumed the coordinatepoints A, A′, A″, B, B′, B″, C, C′ and C″ and their respective contourlines portions have already satisfied the tangent angle criterion w_(c)of block 2508.

As can be understood from FIGS. 45N-P, points A, A′ and A″ are in closeproximity to each other due to the close proximity of their respectivecontour line segments. The close proximity of the respective contourlines is a result of the rise or fall distances d_(AA′) and d_(A′A″)being small at points A, A′ and A″, as the contour lines m^(th),m^(th+1) and m^(th+2) at all points A, A′, A″, B, B′, B″, C, C′ and C″are evenly spaced medially-laterally due to having equal slicethicknesses D_(T). Due to the close proximity of points A, A′ and A″,line segments AA′ and A′A″ are relatively short, resulting in angulardeviations θ_(AA′) and θ_(A′A″) that are less than the angular criterionθ_(c), which in one embodiment, may be in the range of approximately oneto approximately four degrees. As the angular deviations θ_(AA′) andθ_(A′A″) are less than the angular criterion θ_(c), the angularcriterion θ_(c) is satisfied for points A, A′ and A″, and these pointsare potential candidates for the generation of the jig's bone matingsurfaces.

As indicated in FIG. 45N, the angular differences φ_(A-A′) and φ_(A′-A″)between the normal vectors NV_(A), NV_(A′) and NV_(A″) is small,resulting in angular differences φ_(A-A′) and φ_(A′-A″) that are lessthan the angular criterion φ_(c), which in one embodiment, may be in therange of approximately two to approximately five degrees. As the angulardifferences φ_(A-A′) and φ_(A′-A″) are less than the angular criterionφ_(c), the angular criterion φ_(c) is satisfied. Because the points A,A′ and A″ have satisfied both of the angular criterion θ_(c) and φ_(c)of block 2514, the surface represented by the points A, A′ and A″ may beemployed to generate the jig's surfaces that matingly contact thepatient's arthroplasty target surfaces per block 2520.

As can be understood from FIGS. 45N and 45Q-R and for reasons similar tothose discussed with respect to points A, A′ and A″, points B, B′ and B″are in close proximity to each other due to the close proximity of theirrespective contour line segments. Consequently, line segments BB′ andB′B″ are relatively short, resulting in angular deviations θ_(BB′) andθ_(B′B″) that are less than the angular criterion θ_(c). As the angulardeviations θ_(BB′) and B_(B′B″) are less than the angular criterionθ_(c), the angular criterion θ_(c) is satisfied for points B, B′ and B″,and these points are potential candidates for the generation of thejig's bone mating surfaces.

As indicated in FIG. 45N, the angular difference φ_(B-B′) between thenormal vectors NV_(B) and NV_(B′) is small such that it is less than theangular criterion φ_(c) and, therefore, satisfies the angular criterionφ_(c). However, the angular difference φ_(B′-B″) between the normalvectors NV_(B′) and NV_(B″) is large such that it is greater than theangular criterion φ_(c) and, therefore, does not satisfy the angularcriterion φ_(c). As the points B and B′ have satisfied both of theangular criterion θ_(c) and φ_(c) of block 2514, the surface representedby the points B and B′ may be employed to generate the jig's surfacesfor matingly contacting the patient's arthroplasty target surfaces perblock 2520. However, as the points B′ and B″ have failed to satisfy bothof the angular criterion θ_(c) and φ_(c) of block 2514, the surfacerepresented by the points B′ and B″ may not be employed to generate thejig's surfaces for matingly contacting the patient's arthroplasty targetsurfaces. Instead, the slice spacing D_(T) may be evaluated per block2516 and reset per block 2518, with the process then started over atblock 2502. Alternatively, the points may be subjected to overestimationper block 2510.

As can be understood from FIGS. 45N and 45S-45T and because ofsignificant rise and fall distances d_(CC′) and d_(C′C″) between thecontour lines at points C, C′ and C″, points C, C′ and C″ are not inclose proximity to each other due to the significant distance betweentheir respective contour line segments. Consequently, line segments CC′and C′C″ are relatively long, resulting in angular deviations θ_(CC′)and θ_(C′C″) that exceed the angular criterion θ_(c) and, therefore, donot satisfy the angular criterion θ_(c).

As indicated in FIG. 45N, the angular differences φ_(C-C′) and φ_(C-C′)between the normal vectors NV_(C), NV_(C′) and NV_(C″) are small suchthat they are less than the angular criterion φ_(c) and, therefore,satisfy the angular criterion φ_(c). However, as the points C, C′ and C″do not satisfied both of the angular criterion θ_(c) and φ_(c), thesurfaces represented by the points C, C′ and C″ may not be employed togenerate the jig's surfaces for matingly contacting the patient'sarthroplasty target surfaces. Instead, the slice spacing D_(T) may beevaluated per block 2516 and reset per block 2518, with the process thenstarted over at block 2502. Alternatively, the points may be subjectedto overestimation per block 2510.

As can be understood from the preceding discussion, in one embodiment,the analysis of the contour lines may be performed slice-by-slice acrossthe series of contour lines. In other words, a first contour linem^(th+1) is compared at its respective coordinate points to thecorresponding coordinate points of the immediate neighbor contour lines(e.g., contour lines m^(th) and m^(th+2)) medial and lateral of thefirst contour line.

While the preceding example process discussed with respect to FIGS.45N-45T is given in the context of three contour lines m^(th), m^(th+1)and m^(th+2) and nine coordinate points A-C″, of course the process canbe readily applied to a greater or less number or contour lines andcoordinate points. Therefore, the process should not be interpreted asbeing limited to any number of contour lines or coordinate points.

For another example application of the functions of block 2514,reference is made to FIGS. 46A-46F. FIGS. 46A, 46C and 46E each depictportions of contour lines n^(th), n^(th+1), n^(th+2), n^(th+3) andn^(th+4) in sagittal views similar to that of FIG. 45C. FIGS. 46B, 46Dand 46F each represent a bone surface contour line 3300 and a linearinterpolation bone surface contour line 3302 as viewed along sectionlines 46B-46B, 46D-46D and 46F-46F transverse to image slices containingthe contour lines n^(th), n^(th+1), n^(th+2), n^(th+3) and n^(th+4) ofrespective FIGS. 46A, 46C and 46E.

As indicated in FIGS. 46A-F, contour lines n^(th), n^(th+1), n^(th+2),n^(th+3) and n^(th+4) each include a respective coordinate point D, D′,D″, D′″ and D″″. In one embodiment, corresponding coordinate points maybe identified via the method discussed above with respect to FIG. 45N.Specifically, as can be understood from FIGS. 46A-B, correspondingcoordinate points D, D′, D″, D′″ and D″″ may be those coordinate pointsD, D′, D″, D′″ and D″″ that each exist in the same medial-lateral planethat is generally perpendicular to the sagittal image slices containingthe contour lines and coordinate points. Other groups of correspondingcoordinate points may be identified via a similar perpendicular planemethodology.

As can be understood from FIGS. 46C-D, corresponding coordinate pointsD, D′, D″, D′″ and D″″ may be identified via a second method.Specifically, the contour lines n^(th), n^(th+1), n^(th+2), n^(th+3) andn^(th+4) may be superimposed into the same image slice layer asindicated in FIG. 46D by arrow 46D1, resulting in a composite plane 46D2having a total rise or fall distance d_(DD″″) between coordinate pointsD and D″″. The total rise or fall distance d_(DD″″) may be the sum ofthe respective rise or fall distances d_(DD′), s_(D′D″), d_(D″D′″),d_(D′″D″″) discussed below with respect to FIGS. 46B, 46C and 46F.

As indicated in FIG. 46C, the normal vector lines NV_(D), NV_(D′),NV_(D″), NV_(D′″) and NV_(D″″), the determination of which is discussedbelow with respect to FIGS. 46A, 46C and 46E, are utilized to identifythe corresponding coordinate points D, D′, D″, D′″ and D″″. For example,the normal vector line NV_(D) of coordinate point D is extended tocontour line n^(th+1), and the intersection between normal vector lineNV_(D) and contour line n^(th+1) identifies the coordinate pointcorresponding to coordinate point D, namely, coordinate point D′. Thenormal vector line NV_(D′) of coordinate point D′ is extended to contourline n^(th+2), and the intersection between normal vector line NV_(D),and contour line n^(th+2) identifies the coordinate point correspondingto coordinate point D′, namely, coordinate point D″. The normal vectorline NV_(D″) of coordinate point D″ is extended to contour linen^(th+3), and the intersection between normal vector line NV_(D″) andcontour line n^(th+3) identifies the coordinate point corresponding tocoordinate point D″, namely, coordinate point D′″. The normal vectorline NV_(D′″) of coordinate point D′″ is extended to contour linen^(th+4), and the intersection between normal vector line NV_(D′″) andcontour line n^(th+4) identifies the coordinate point corresponding tocoordinate point D′″, namely, coordinate point D″″. Other groups ofcorresponding coordinate points may be identified via a normal vectorline methodology.

As can be understood from FIGS. 46F-E, corresponding coordinate pointsD, D′, D″, D′″ and D″″ may be identified via a third method.Specifically, the contour lines n^(th), n^(th+1), n^(th+2), n^(th+3) andn^(th+4) may be superimposed into the same image slice layer asindicated in FIG. 46F by arrow 46D1, resulting in a composite plane 46D2having a total rise or fall distance between coordinate points D andD″″. The total rise or fall distance may be the sum of the respectiverise or fall distances d_(DD′), d_(D′D″), d_(D″D′″), d_(D′″D″″)discussed below with respect to FIGS. 46B, 46C and 46F.

As indicated in FIG. 46E, a center point CP is identified. The centerpoint CP may generally correspond to an axis extending generallyperpendicular to the sagittal image slices. The center point CP may beconsidered to be a center point generally common to the curvature of allof the contour lines n^(th), n^(th+1), n^(th+2), n^(th+3) and n^(th+4)and about which all of the contour lines n^(th), n^(th+1), n^(th+2),n^(th+3) and n^(th+4) and arcuately extend.

As shown in FIG. 46E, radius lines R, R′, R″, etc. may radially extendin a straight line from the center point CP across the contour linesn^(th), n^(th+1), n^(th+2), n^(th+3) and n^(th+4). As can be understoodfrom radius line R, the corresponding coordinate lines D, D′, D″, D′″and D″″ are identified where radius line R intersects each respectivecontour lines n^(th), n^(th+1), n^(th+2), n^(th+3) and n^(th+4). Othergroups of corresponding coordinate points may be identified with radiuslines R′, R″ and etc.

Once the corresponding coordinate points D, D′, D″, D′″ and D″″ areidentified via any of the three methods, the extent of the surfacevariation between the corresponding coordinate points D, D′, D″, D′″ andD″″ may be analyzed as follows.

As can be understood from FIGS. 46A-46F, each coordinate point D, D′,D″, D′″ and D″″ includes a respective tangent line t_(D), t_(D′),t_(D″), t_(D′″) and t_(D″″) that is tangent to the corresponding contourline n^(th), n^(th+1), n^(th+2), n^(th+3) and n^(th+4) at the coordinatepoint D, D′, D″, D′″ and D″″, each tangent line t_(D), t_(D′), t_(D″),t_(D′″) and t_(D″″) being parallel to and contained within the imageslice of its contour line. A vector line NV_(D), NV_(D′) and NV_(D″),NV_(D′″) and NV_(D″″) extends normally from each respective tangent linet_(D), t_(D′), t_(D″), t_(D′″) and t_(D″″) at each respective coordinatepoint D, D′, D″, D′″ and D″″. Line segments DD′, D′D″, D″D′″ and D′″D″″extend between their associated coordinate points to create a linearinterpolation 3302 of the bone contour line 3300.

In this example, it is assumed the coordinate points D, D′, D″, D′″ andD″″ and their respective contour lines portions have already satisfiedthe tangent angle criterion w_(c) of block 2508. For example, point Dmay be point k of potential mating region 2402A of contour line 2400 inFIG. 45D, and coordinate points D′-D″″ may be points on contour lines ofadjacent image slices, wherein coordinate points D′-D″″ are identifiedas coordinate points corresponding to coordinate point D. Each of thecoordinate points D, D′, D″, D′″ and D″″ is then evaluated to determineif the criterion of θ_(c) and φ_(c) of block 2514 are satisfied too.

As can be understood from FIGS. 46B, 46D and 46F, points D″, D′″ and D″″are in close proximity to each other due to the close proximity of theirrespective contour line segments. The close proximity of the respectivecontour lines is a result of the rise or fall distances d_(D″D′″) andd_(D′″D″″) being small at points D″, D′″ and D″″, as the contour linesn^(th), n^(th+1), n^(th+2), n^(th+3) and n^(th+4) at all points D, D′,D″, D′″ and D″″ are evenly spaced medially-laterally due to having equalslice thicknesses D_(T), which, for example, may be a slice thicknessD_(T) of 2 mm. Due to the close proximity of points D″, D′″ and D″″,line segments D″D′″ and D′″D″″ range in size from relatively short tonearly zero, resulting in angular deviations θ_(D″D′″) and θ_(D′″D″″)that are less than the angular criterion θ_(c), which in one embodiment,may be in the range of approximately one to approximately four degrees.As the angular deviations θ_(D″D′″) and θ_(D′″D″″) are less than theangular criterion θ_(c), the angular criterion θ_(c) is satisfied forpoints D″, D′″ and D″″, and these points are potential candidates forthe generation of the jig's bone mating surfaces. As can be understoodfrom FIGS. 46B, 46D and 46F, the angular deviations θ_(D″D′″) andθ_(D′″D″″) being less than the angular criterion θ_(c) results in thecorresponding line segments D″D′″ and D′″D″″ closely approximating thecontour of the bone surface 3300.

As indicated in FIGS. 46A, 46C and 46E, the angular differencesφ_(D″-D′″) and φ_(D′″-D″″) between the normal vectors NV_(D″), NV_(D′″)and NV_(D″″) is small, resulting in angular differences φ_(D″-D′″) andφ_(D′″-D″″) that are less than the angular criterion φ_(c), which in oneembodiment, may be in the range of approximately two to approximatelyfive degrees. As the angular differences φ_(D″-D′″) and φ_(D′″-D″″) areless than the angular criterion φ_(c), the angular criterion φ_(c) issatisfied. As can be understood from the tangent lines t_(D″), t_(D′″)and t_(D″″) depicted in FIGS. 46A, 46C and 46E, the contour line slopesat the respective coordinate points D″, D′″ and D″″ are nearlyidentical, indicating that there is little surface variation between thecoordinate points and the coordinate points would be a closeapproximation of the actual bone surface.

Because the points D″, D′″ and D″″ have satisfied both of the angularcriterion θ_(c) and φ_(c) of block 2514, the surface represented by thepoints D″, D′″ and D″″ may be employed to generate the jig's surfacesthat matingly contact the patient's arthroplasty target surfaces perblock 2520.

As can be understood from FIGS. 46B, 46D and 46F and because ofsignificant rise and fall distances d_(DD′) and d_(D′D″) between thecontour lines at points D, D′ and D″, points D, D′ and D″ are not inclose proximity to each other due to the significant distance betweentheir respective contour line segments. Consequently, line segments DD′and D′D″ are relatively long, resulting in angular deviations θ_(DD′)and θ_(D′D″) that exceed the angular criterion θ_(c) and, therefore, donot satisfy the angular criterion θ_(c). As the angular deviationsθ_(D″D′″) and θ_(D′″-D″″) are greater than the angular criterion θ_(c),the angular criterion θ_(c) is not satisfied for points D, D′ and D″,and these points are not potential candidates for the generation of thejig's bone mating surfaces. As can be understood from FIGS. 46B, 46D and46F, the angular deviations θ_(DD′) and θ_(D′D″) being greater than theangular criterion θ_(c) results in the corresponding line segments DD′and D′D″ not closely approximating the contour of the bone surface 3300.

As indicated in FIGS. 46A, 46C and 46E, the angular differences φ_(D-D′)and φ_(D′_D″) between the normal vectors NV_(D) and NV_(D′) and NV_(D′)and NV_(D″) are large such that they are greater than the angularcriterion φ_(c) and, therefore, do not satisfy the angular criterionφ_(c). Thus, as the points D, D′ and D″ do not satisfied both of theangular criterion θ_(c) and φ_(c), the surfaces represented by thepoints D, D′ and D″ may not be employed to generate the jig's surfacesfor matingly contacting the patient's arthroplasty target surfaces.Instead, the slice spacing D_(T) may be evaluated per block 2516 andreset per block 2518, with the process then started over at block 2502.Alternatively, the points may be subjected to overestimation per block2510.

FIG. 46G is a distal view similar to that of FIGS. 42A and 45A depictingcontour lines 3400 produced by imaging the right femur at an imagespacing D_(T) of, for example, 2 mm. As shown, the contour lines 3400may be grouped into multiple regions in the lateral-medial direction3402-3408 for the sake of discussion. The region 3402 includes thecontour lines 3400 of the most lateral half of the femoral lateralcondyle and extends medially from the most lateral side of the femorallateral condyle to the medial-lateral middle of the femoral lateralcondyle. The region 3404 includes the contour lines 3400 of the mostmedial half of the femoral lateral condyle and extends medially from themiddle of the femoral lateral condyle to the medial-lateral center ofintercondylar notch. The region 3406 includes the contour lines 3400 ofthe most lateral half of the femoral medial condyle and extends mediallyfrom the medial-lateral center of the intercondylar notch to themedial-lateral middle of the femoral medial condyle. The region 3408includes the contour lines 3400 of the most medial half of the femoralmedial condyle and extends medially from the medial-lateral middle ofthe femoral medial condyle to the most medial side of the femoral medialcondyle.

FIG. 46H is a sagittal view of the contour lines 3400 of region 3402 ofFIG. 46G. The contour lines 3400 of region 3402 include contour lines3502, 3503, 3504, 3505, 3506, 3507 and 3508, with the most lateralportion of the femoral lateral condyle being indicated by contour line3502. The size of each successive contour line 3400 of region 3402increases moving medially from the most lateral contour line 3502 ofregion 3402 to the most medial contour line 3508 of region 3402, whichis near the medial-lateral middle of the lateral condyle.

As can be understood from FIG. 46H, the contour lines 3502-3504 arespaced apart from their respective adjacent contour lines a substantialamount around their entire boarders. Such wide spacing corresponds to asubstantial amount of rise or fall distances between adjacent contourlines, as discussed above with respect to FIG. 46B. Thus, such contourlines would likely fail to meet the angular criterion θ_(c) and besubject to the overestimation process such that jig surfacescorresponding to the contour lines 3502-3504 would not contact thecorresponding surfaces of the arthroplasty target areas.

As can be understood from FIG. 46H, in the distal portion of the femoralcondyle, the contour lines 3505-3508 in the region 3510 converge suchthat there is little, if any, amount of rise or fall distance betweenadjacent contour lines. Thus, such contour lines 3505-3508 in the region3510 would likely meet the first angular criterion θ_(c).

As can be understood from the arrows in region 3510, the angulardifferences between normal vectors for the contour line portions withinthe region 3510 would be minimal, likely meeting the second angularcriterion φ_(c). Thus, as the portions of the contour lines 3505-3508within region 3510 likely meet both angular criterion θ_(c) and φ_(c),the portions of the contour lines 3505-3508 within the region 3510represent an optimal contact area 3510 for mating contact with the jig'sbone mating surface 40. In one embodiment, as can be understood fromFIG. 47A discussed below, the optimal contact area 3510 may be thelateral half of the surface of the lateral condyle that displacesagainst the recess of the lateral tibia plateau.

In one embodiment, the optimal contact area 3510 matingly corresponds tothe jig's bone mating surface 40 such that the portions of the contourlines 3402 indicated by region 3510 may be used to generate the jig'sbone mating surface 40, per the algorithm 2500 of FIG. 45E. Conversely,per the algorithm 2500, the portions of the contour lines 3402 outsideregion 3510 may be subjected to the overestimation process discussedabove such that the jig's surfaces created from the overestimatedcontour line portions results in jig surfaces that do not contact thecorresponding portions of the patient's arthroplasty target regions.

FIG. 46I is a sagittal view of the contour lines 3400 of region 3404 ofFIG. 46G. The contour lines 3400 of region 3404 include contour lines3602, 3603, 3604, 3605, 3606, 3607, 3608, 3609 and 3610 with the mostlateral portion of region 3404 being indicated by contour line 3602,which is near the medial-lateral middle of the lateral condyle, and themost medial portion of region 3404 being indicated by contour line 3610,which is near the medial-lateral center of intercondylar notch. The sizeof each successive contour line 3400 of region 3404 decreases movingmedially from the most lateral contour line 3602 to the most medialcontour line 3610.

As can be understood from FIG. 46I, the contour lines 3607-3610 arespaced apart from their respective adjacent contour lines a substantialamount in their posterior portions and to a lesser extent in theirdistal portions, these distal portions corresponding to theintercondylar notch and trochlear groove. Such wide spacing correspondsto a substantial amount of rise or fall distances between adjacentcontour lines, as discussed above with respect to FIG. 46B. Thus, suchcontour lines would likely fail to meet the angular criterion θ_(c) andbe subject to the overestimation process such that jig surfacescorresponding to the contour lines 3607-3610 would not contact thecorresponding surfaces of the arthroplasty target areas.

As can be understood from FIG. 46I, in the distal portion of the femoralcondyle, the contour lines 3602-3606 in the region 3614 converge suchthat there is little, if any, amount of rise or fall distance betweenadjacent contour lines. Similarly, in the anterior condylar portion ofthe distal femur, the contour lines 3602-3606 in the region 3616converge such that there is little, if any, amount of rise or falldistance between adjacent contour lines. Thus, such contour lines3602-3606 in the regions 3614 and 3616 would likely meet the firstangular criterion θ_(c).

As can be understood from the arrows in regions 3614 and 3616, theangular differences between normal vectors for the contour line portionswithin the regions 3614 and 3616 would be minimal, likely meeting thesecond angular criterion φ_(c). Thus, as the portions of the contourlines 3602-3606 within regions 3614 and 3616 likely meet both angularcriterion θ_(c) and φ_(c), the portions of the contour lines 3602-3606within the regions 3614 and 3616 represent optimal contact areas 3614and 3616 for mating contact with the jig's bone mating surface 40.

In one embodiment, the optimal contact areas 3614 and 3616 matinglycorrespond to the jig's bone mating surface 40 such that the portions ofthe contour lines 3404 indicated by regions 3614 and 3616 may be used togenerate the jig's bone mating surface 40, per the algorithm 2500 ofFIG. 45E. Conversely, per the algorithm 2500, the portions of thecontour lines 3404 outside regions 3614 and 3616 may be subjected to theoverestimation process discussed above such that the jig's surfacescreated from the overestimated contour line portions results in jigsurfaces that do not contact the corresponding portions of the patient'sarthroplasty target regions.

In one embodiment, as can be understood from FIG. 47A discussed below,the optimal contact area 3614 may be the medial half of the surface ofthe lateral condyle that displaces against the recess of the lateraltibia plateau. In one embodiment, as can be understood from FIG. 47Adiscussed below, the optimal contact area 3616 may be the lateral halfof a generally flat surface of the anterior condyle, wherein the flatsurface is located in an area proximal the concave trochlear groove ofthe patellar face and extends to a point near the anterior portion ofthe femoral shaft.

FIG. 46J is a sagittal view of the contour lines 3400 of region 3406 ofFIG. 46G. The contour lines 3400 of region 3406 include contour lines3702, 3703, 3704, 3705, 3706, 3707, 3708, 3709 and 3710 with the mostlateral portion of region 3404 being indicated by contour line 3702,which is near the medial-lateral center of intercondylar notch, and themost medial portion of region 3406 being indicated by contour line 3710,which is near the medial-lateral middle of the medial condyle. The sizeof each successive contour line 3400 of region 3406 increases movingmedially from the most lateral contour line 3702 to the most medialcontour line 3710.

As can be understood from FIG. 46J, the contour lines 3702-3706 arespaced apart from their respective adjacent contour lines a substantialamount in their posterior portions and to a lesser extent in theirdistal portions, these distal portions corresponding to theintercondylar notch and trochlear groove. Such wide spacing correspondsto a substantial amount of rise or fall distances between adjacentcontour lines, as discussed above with respect to FIG. 46B. Thus, suchcontour lines would likely fail to meet the angular criterion θ_(c) andbe subject to the overestimation process such that jig surfacescorresponding to the contour lines 3607-3610 would not contact thecorresponding surfaces of the arthroplasty target areas.

As can be understood from FIG. 46J, in the distal portion of the femoralcondyle, the contour lines 3707-3710 in the region 3714 converge suchthat there is little, if any, amount of rise or fall distance betweenadjacent contour lines. Similarly, in the anterior condylar portion ofthe distal femur, the contour lines 3707-3710 in the region 3716converge such that there is little, if any, amount of rise or falldistance between adjacent contour lines. Thus, such contour lines3707-3710 in the regions 3714 and 3716 would likely meet the firstangular criterion θ_(c).

As can be understood from the arrows in regions 3714 and 3716, theangular differences between normal vectors for the contour line portionswithin the regions 3714 and 3716 would be minimal, likely meeting thesecond angular criterion φ_(c). Thus, as the portions of the contourlines 3707-3710 within regions 3714 and 3716 likely meet both angularcriterion θ_(c) and φ_(c), the portions of the contour lines 3707-3710within the regions 3714 and 3716 represent optimal contact areas 3714and 3716 for mating contact with the jig's bone mating surface 40.

In one embodiment, the optimal contact areas 3714 and 3716 matinglycorrespond to the jig's bone mating surface 40 such that the portions ofthe contour lines 3406 indicated by regions 3714 and 3716 may be used togenerate the jig's bone mating surface 40, per the algorithm 2500 ofFIG. 45E. Conversely, per the algorithm 2500, the portions of thecontour lines 3406 outside regions 3714 and 3716 may be subjected to theoverestimation process discussed above such that the jig's surfacescreated from the overestimated contour line portions results in jigsurfaces that do not contact the corresponding portions of the patient'sarthroplasty target regions.

In one embodiment, as can be understood from FIG. 47A discussed below,the optimal contact area 3714 may be the lateral half of the surface ofthe medial condyle that displaces against the recess of the medial tibiaplateau. In one embodiment, as can be understood from FIG. 47A discussedbelow, the optimal contact area 3716 may be the medial half of agenerally flat surface of the anterior condyle, wherein the flat surfaceis located in an area proximal the concave trochlear groove of thepatellar face and extends to a point near the anterior portion of thefemoral shaft.

FIG. 46K is a sagittal view of the contour lines 3400 of region 3408 ofFIG. 46G. The contour lines 3400 of region 3408 include contour lines3802, 3803, 3804, 3805, 3806, 3807, 3808, 3809, 3810, 3811 and 3812,with the most medial portion of the femoral lateral condyle beingindicated by contour line 3812. The size of each successive contour line3400 of region 3408 decreases moving medially from the most lateralcontour line 3802 of region 3408, which is near the medial-lateralmiddle of the medial condyle, to the most medial contour line 3812 ofregion 3408.

As can be understood from FIG. 46K, the contour lines 3810-3812 arespaced apart from their respective adjacent contour lines a substantialamount around their entire boarders. Such wide spacing corresponds to asubstantial amount of rise or fall distances between adjacent contourlines, as discussed above with respect to FIG. 46B. Thus, such contourlines would likely fail to meet the angular criterion θ_(c) and besubject to the overestimation process such that jig surfacescorresponding to the contour lines 3810-3812 would not contact thecorresponding surfaces of the arthroplasty target areas.

As can be understood from FIG. 46K, in the distal portion of the femoralcondyle, the contour lines 3802-3809 in the region 3814 converge suchthat there is little, if any, amount of rise or fall distance betweenadjacent contour lines. Thus, such contour lines 3802-3809 in the region3814 would likely meet the first angular criterion θ_(c).

As can be understood from the arrows in region 3814, the angulardifferences between normal vectors for the contour line portions withinthe region 3814 would be minimal, likely meeting the second angularcriterion φ_(c). Thus, as the portions of the contour lines 3802-3809within region 3814 likely meet both angular criterion θ_(c) and φ_(c),the portions of the contour lines 3802-3809 within the region 3814represent an optimal contact area 3814 for mating contact with the jig'sbone mating surface 40. In one embodiment, as can be understood fromFIG. 47A discussed below, the optimal contact area 3814 may be themedial half of the surface of the medial condyle that displaces againstthe recess of the medial tibia plateau.

In one embodiment, the optimal contact area 3814 matingly corresponds tothe jig's bone mating surface 40 such that the portions of the contourlines 3408 indicated by region 3814 may be used to generate the jig'sbone mating surface 40, per the algorithm 2500 of FIG. 45E. Conversely,per the algorithm 2500, the portions of the contour lines 3408 outsideregion 3814 may be subjected to the overestimation process discussedabove such that the jig's surfaces created from the overestimatedcontour line portions results in jig surfaces that do not contact thecorresponding portions of the patient's arthroplasty target regions.

As can be understood from the preceding discussion, the overestimationprocess disclosed herein can be used to identifying optimal target areas(e.g., optimal target areas 3510, 3614, 3616, 3714, 3716 and 3814 asdiscussed with respect to FIGS. 46H-46K). More specifically, theoverestimation process disclosed herein can employ these optimal targetareas to generate the bone mating surfaces 40 of the jigs 2 whilecausing the other surface areas of the jigs to be configured such thatthese other jig surface areas will not contact the surfaces of thearthroplasty target areas when the jig's bone mating surfaces 40 havematingly received and contacted the arthroplasty target areas. Theresult is a jig that has bone mating surfaces 40 that are based on theregions of the arthroplasty target region that are most accuratelyrepresented via 3D computer modeling and most likely to be machinableinto the jig. Such a jig provides an increased accuracy of fit betweenthe jig's mating surface 40 and the arthroplasty target areas of thepatient's bone.

For most patients, it is common that the overestimation process outlinedin FIG. 45E will result in certain areas of the femoral arthroplastytarget region being identified as the optimal target areas discussedabove with respect to FIGS. 46H-46K. For example, as depicted in FIG.47A, which is distal-sagittal isometric view of a femoral distal end3900, a commonly encountered, healthy, non-deformed femoral distal end3900 may have an arthroplasty target region 3902 with certain optimaltarget regions 3904, 3906 and 3908. These optimal target regions 3904,3906 and 3908 commonly identified on most patients via theoverestimation process of FIG. 45E are indicated in FIG. 47A by thecross-hatched regions. It has been found that these optimal targetregions 3904, 3906 and 3908 are the regions of the arthroplasty targetregion 3902 that are most likely to satisfy the criterion w_(i), θ_(c)and φ_(c) of blocks 2508 and 2514 of FIG. 45E. Therefore, these targetregions 3904, 3906 and 3908 may be used to generate the jig's bonemating surfaces 40.

While, in one embodiment, the overestimation process of FIG. 45E islikely to result in optimal target regions such as those indicated viathe cross-hatching 3904, 3906 and 3908, in other embodiments, theoptimal target regions may result in target regions in other locationson the femoral distal end 3900 that are in addition to, or in place of,those regions 3904, 3906 and 3908 depicted in FIG. 47A.

One of the benefits of the overestimation process of FIG. 45E is that itidentifies two types of contour lines 210 y, the first type being thosecontour lines that are most likely to be unacceptable for the generationa jig's bone mating surfaces 40, and the second type being those contourlines that are most likely to be acceptable for the generation of ajig's bone mating surfaces 40. The first type of contour lines areunlikely to be acceptable for the generation of a jig's bone matingsurfaces 40 because they pertain to bone surfaces that are too varied tobe accurately 3D computer modeled and/or are such that they are notreadily machinable into the jig blank. Conversely, the second type ofcontour lines are likely to be acceptable for the generation of a jig'sbone mating surfaces 40 because they pertain to bone surfaces that varysuch an insubstantial amount that they can be accurately 3D computermodeled and are such that they are readily machinable into the jigblank. While optimal target regions 3904, 3906 and 3908 representregions likely corresponding to contour lines of the second type formost commonly encountered patients, the overestimation processesdisclosed herein may be adapted to result in other or additional optimaltarget regions.

In some instances the entirety of the target regions 3904, 3906 and 3908may correspond to the second type of contour lines, namely those type ofcontour lines that satisfy the criterion w_(i), θ_(c) and φ_(c) ofblocks 2508 and 2514 of FIG. 45E. In such instances, the entirety of thetarget regions 3904, 3906 and 3908 are matingly contacted by the jig'sbone mating surface 40.

However, in some instances one or more potions of one or more of thetarget regions 3904, 3906 and 3908 may be subjected to overestimation sothat the jig's bone mating surface 40 does not contact such portions ofthe target regions 3904, 3906 and 3908, although the jig's bone matingsurface 40 still matingly contacts the other portions of the targetregions 3904, 3906 and 3908 corresponding to the second type of contourlines. Such a situation may arise, for example, where a substantialsurface variation (e.g., a hole, deformity or osteophyte) exists on acondyle articular surface 3918, 3919 that meets the criterion w_(i),θ_(c) and φ_(c) of blocks 2508 and 2514 for the rest of its surface.

The overestimation process disclosed herein may result in theidentification of target regions 3904, 3906, 3908 that are most likelyto result in bone mating surfaces 40 of jigs 2 that are readilymachinable into the jigs 2 and most likely to facilitate reliable andaccurate mating of the jigs to the arthroplasty target regions. Theoverestimation process results in such accurate and reliable bone matingsurfaces 40 while causing other surfaces of the jigs 2 corresponding toless predictable bone surfaces to not contact the bone surfaces when thebone mating surfaces 40 matingly receive the target regions 3904, 3906,3908 of the actual arthroplasty target region.

As indicated in FIG. 47A by the cross-hatched regions, optimal targetregions 3904, 3906 and 3908 may include three general areas of thefemoral condyle 3910. For example, the anterior optimal target region3904 may include the anterior portion of the femoral distal end 3900just proximal of the condyle 3910 region, the lateral optimal targetregion 3906 may include the distal portion of the lateral condyle 3912,and the medial optimal target region 3908 may include the distal portionof the medial condyle 3914.

As indicated in FIG. 47A, the femoral distal end 3900 may include alateral condyle 3912 and a lateral epicondyle 3913, a medial condyle3914 and a medial epicondyle 3915, a intercondylar notch 3939 and atrochlear groove 3916 of the patellar surface separating the twocondyles 3912 and 3914, and a femoral shaft 3917 extending distally fromthe condyle region 3910. Each condyle 3912 and 3914 includes anarticular surface 3918 and 3919 that articulates against correspondingarticular surfaces of the tibia plateau.

As indicated in FIG. 47D, which is a coronal view of the anterior sideof the femoral distal end 3900, the articular surfaces of the condyles3914, 3912 and the trochlear groove 3916 transition into each other toform a patellar facet 39D1 that has an anterior boarder or seam 39D2.Proximal of the patellar facet boarder 39D2 and identified by a dashedline is the capsular line 39D3 extending medial-lateral in an arc. Theadductor tubercle is indicated at 39D4, the fibular lateral ligament at39D5, the popliteus at 39D6, the vastus intermedius at 39D7, and thearticular genu at 39D8.

As indicated in FIG. 47A by the cross-hatching, in one embodiment, thelateral optimal target region 3906 may be generally coextensive with thelateral condyle articular surface 3918 that articulates against therespective articulate surface of the tibia plateau. In one embodiment,the lateral optimal target region 3906 may extend: anterior-posteriorbetween the anterior end 3920 and posterior end 3921 of the lateralarticular condyle surface 3918; and lateral-medial between the lateralside 3922 and medial side 3923 of the lateral articular condyle surface3918. In one embodiment, the lateral optimal target region 3906generally begins near the anterior-distal end 3920 of the lateralcondyle 3912 outside the trochlear groove 3916 of the patellar surfaceand ends near the posterior-distal end 3921 of the lateral condyle 3912.In one embodiment as can be understood from FIG. 47A, the lateraloptimal target region 3906 may be the entire cross-hatched region 3906or any one or more portions of the cross-hatched region 3906.

In one embodiment as indicated in FIG. 47A by the double cross-hatching,an anterior target area 3906A and a distal target area 3906D may beidentified within the lateral optimal target region 3906 via theoverestimation process disclosed herein. Thus, although the lateraloptimal target region 3906 may be generally coextensive with the lateralcondyle articular surface 3918, the actual areas within the lateraloptimal target region 3906 identified as being reliable surfaces for thegeneration of the mating surfaces of arthroplasty jigs may be limited toan anterior target area 3906A and a distal target area 3906D, theremainder of the lateral optimal target region 3906 being subjected tothe overestimation process. The anterior target area 3906A may belocated in the anterior third of the lateral optimal target region 3906,and the distal target area 3906D may be located near a most distal pointof the lateral optimal target region 3906.

As indicated in FIG. 47A by the cross-hatching, in one embodiment, themedial optimal target region 3908 may be generally coextensive with themedial condyle articular surface 3919 that articulates against therespective articulate surface of the tibia plateau. Specifically, in oneembodiment, the medial optimal target region 3908 may extend:anterior-posterior between the anterior end 3924 and posterior end 3925of the medial articular condyle surface 3919; and lateral-medial betweenthe lateral side 3926 and medial side 3927 of the medial articularcondyle surface 3919. In one embodiment, the medial optimal targetregion 3908 generally begins near the anterior-distal end 3924 of themedial condyle 3914 outside the trochlear groove 3916 of the patellarsurface and ends near the posterior-distal end 3925 of the medialcondyle 3914. In one embodiment as can be understood from FIG. 47A, themedial optimal target region 3908 may be the entire cross-hatched region3908 or any one or more portions of the cross-hatched region 3908.

In one embodiment as indicated in FIG. 47A by the double cross-hatching,an anterior target area 3908A and a distal target area 3908D may beidentified within the medial optimal target region 3908 via theoverestimation process disclosed herein. Thus, although the medialoptimal target region 3908 may be generally coextensive with the medialcondyle articular surface 3919, the actual areas within the medialoptimal target region 3908 identified as being reliable surfaces for thegeneration of the mating surfaces of arthroplasty jigs may be limited toan anterior target area 3908A and a distal target area 3908D, theremainder of the medial optimal target region 3908 being subjected tothe overestimation process. The anterior target area 3908A may belocated in the anterior third of the medial optimal target region 3908,and the distal target area 3908D may be located near a most distal pointof the medial optimal target region 3908.

As indicated in FIG. 47A by the cross-hatching, in one embodiment, theanterior optimal target region 3904 may be a generally planar area ofthe anterior side of the femoral shaft 3917 proximally adjacent thecondyle portion 3910 of the femoral distal end 3900. In other words, theanterior optimal target region 3904 may be a generally planar area ofthe anterior side of the femoral shaft 3917 proximally adjacent theanterior end 3940 of the trochlear groove 3916.

As shown in FIG. 47D by the cross-hatching, in one embodiment, theanterior optimal target region 3904 may be located in a generally planarsurface region of the anterior side of the femoral shaft 3917 generallydistal of the articularis genu 39D8 and generally proximal of thepatellar facet boarder 39D2. In one embodiment, the anterior optimaltarget region 3904 may be located in a generally planar surface regionof the anterior side of the femoral shaft 3917 generally distal of thearticularis genu 39D8 and generally proximal of the capsular line 39D3.In either case, the anterior optimal target region 3904 may be generallycentered medial-lateral on the anterior side of the femoral shaft 3917.

As can be understood from FIG. 47A, in one embodiment, the anteriortarget region 3904 may have a lateral-medial dimension of approximatelyone centimeter to approximately seven centimeters. In one embodiment,the anterior optimal target region 3904 may be approximately centered ona line that: is generally parallel to the femoral anatomical axis; andextends from the center of the trochlear groove 3916. In one embodiment,the medial-lateral width of the anterior optimal target region 3904 maybe medially-laterally bounded by lines extending generally parallel tothe femoral anatomical axis from the most medial and most lateralboundaries of the trochlear groove 3916. In one embodiment as can beunderstood from FIG. 47A, the anterior target region 3904 may be theentire cross-hatched region 3904 or any one or more portions of thecross-hatched region 3904.

In one embodiment as indicated in FIGS. 47A and 47D by the doublecross-hatching, an anterior target area 3904A may be identified withinthe anterior optimal target region 3904 via the overestimation processdisclosed herein. Thus, although the anterior optimal target region 3904may be generally coextensive with the generally planar surface areabetween the articularis genu 39D8 and the capsular line 39D3, the actualareas within the anterior optimal target region 3904 identified as beinga reliable surface for the generation of the mating surfaces ofarthroplasty jigs may be limited to an anterior target area 3904A, theremainder of the anterior optimal target region 3904 being subjected tothe overestimation process. The anterior target area 3904A may belocated any where within the anterior optimal target region 3904.

FIG. 47B is bottom perspective view of an example customizedarthroplasty femoral jig 2A that has been generated via theoverestimation process disclosed herein. Similar to the femoral jig 2Adepicted in FIGS. 1G and 1F, the femoral jig 2A of FIG. 47B includes aninterior or bone-facing side 100 and an exterior side 102. When the jig2A is mounted on the arthroplasty target region during a surgicalprocedure, the bone-facing side 100 faces the surface of thearthroplasty target region while the exterior side 102 faces in theopposite direction.

The interior or bone-facing side 100 of the femur cutting jig 2Aincludes bone mating surfaces 40-3904, 40-3906 and 40-3908 that: aremachined into the jig interior or bone-facing side 100 based on contourlines that met the criterion of blocks 2508 and 2514 of FIG. 45E; andrespectively correspond to the optimal target regions 3904, 3906 and3908 of FIG. 47A. The rest 100′ of the interior or bone-facing side 100(i.e., the regions 100′ of the interior or bone facing sides 100 outsidethe bounds of bone mating surfaces 40-3904, 40-3906 and 40-3908) are theresult of the overestimation process wherein the corresponding contourlines failed to meet one or more of the criterion of blocks 2508 and2514 of FIG. 45E and, consequently, were moved away from the bonesurface. As a result, the interior side surface 100′ is machined to bespaced away from the bone surfaces of the arthroplasty target region soas to not contact the bone surfaces when the bone mating surfaces40-3904, 40-3906 and 40-3908 matingly receive and contact the bonesurfaces of the arthroplasty target region corresponding to regions3904, 3906 and 3908.

As can be understood from FIG. 47B, depending on the patient's bonetopography, the overestimation process disclosed herein may result inbone mating surfaces 40-3904, 40-3906 and 40-3908 that are actuallymultiple bone mating surfaces and/or substantially smaller than depictedin FIG. 47B. For example, the lateral condyle bone mating surface40-3906 may actually be an anterior lateral condyle bone mating surface40-3906A and a distal lateral condyle bone mating surface 40-3906D, withthe areas of the lateral condyle bone mating surface 40-3906 outside theanterior and distal bone mating surfaces 40-3906A and 40-3906D being theresult of the overestimation process so as to not contact thecorresponding bone surfaces when the anterior and distal mating surfaces40-3906A and 40-3906D matingly receive and contact their respectivecorresponding bone surfaces. The anterior and distal bone matingsurfaces 40-3906A and 40-3906D may be configured and positioned in thejig inner surface 100 to matingly receive and contact the anterior anddistal optimal target areas 3906A and 3906D discussed above with respectto FIG. 47A.

As can be understood from FIG. 47B, the medial condyle bone matingsurface 40-3908 may actually be an anterior medial condyle bone matingsurface 40-3908A and a distal medial condyle bone mating surface40-3908D, with the areas of the medial condyle bone mating surface40-3908 outside the anterior and distal mating surfaces 40-3908A and40-3908D being the result of the overestimation process so as to notcontact the corresponding bone surfaces when the anterior and distalbone mating surfaces 40-3908A and 40-3908D matingly receive and contacttheir respective corresponding bone surfaces. The anterior and distalbone mating surfaces 40-3908A and 40-3908D may be configured andpositioned in the jig inner surface 100 to matingly receive and contactthe anterior and distal optimal target areas 3908A and 3908D discussedabove with respect to FIG. 47A.

As can be understood from FIG. 47B, the anterior shaft bone matingsurface 40-3904 may actually be a smaller anterior shaft bone matingsurface 40-3904A, with the area of the anterior shaft bone matingsurface 40-3904 outside the smaller anterior mating surface 40-3904Abeing the result of the overestimation process so as to not contact thecorresponding bone surface when the smaller anterior mating surface40-3904A matingly receives and contacts its corresponding bone surface.The smaller anterior bone mating surface 40-3904A may be configured andpositioned in the jig inner surface 100 to matingly receive and contactthe anterior optimal target area 3904A discussed above with respect toFIGS. 47A and 47D.

As can be understood from FIG. 47C, which is a anterior-posteriorcross-section of the femur jig 2A of FIG. 47B mounted on the femurdistal end 3900 of FIG. 47A, the interior or bone-facing side 100 isformed of bone mating surfaces 40-3904, 40-3906 and 40-3908 andspaced-apart surfaces 100′ (i.e., bone-facing surfaces 100 that are aproduct of the overestimation process and are spaced-apart from thecorresponding bone surfaces of the arthroplasty target region 3902). Asindicated by the plurality of opposed arrows in regions 3984, 3986 and3988, the bone mating surfaces 40-3904, 40-3906 and 40-3908 matinglyreceive and contact the corresponding bone surfaces 3904, 3906 and 3908to form mating surface contact regions 3984, 3986 and 3988. Conversely,the spaced-apart surfaces 100′ are spaced apart from the correspondingbone surfaces to form spaced-apart non-contact regions 3999, wherein thespaced-apart surfaces 100′ do not contact their corresponding bonesurfaces. In addition to having the mating surfaces 40-3904, 40-3906 and40-3908 and the spaced-apart surfaces 100′, the femur jigs 2A may alsohave a saw cutting guide slot 30 and anterior and posterior drill holes45N and 32P, as discussed above.

The arrows in FIG. 47C represent a situation where the patient's bonetopography and the resulting overestimation process has generated bonemating surfaces 40-3904, 40-3906 and 40-3908 that match the targetregions 3904, 3906 and 3908, which are generally coextensive with theentirety of their respective potential regions as discussed above. Ofcourse, where the patient's bone topography and the resultingoverestimation process generates bone mating surfaces 40-3904A,40-3906A, 40-3906D, 40-3908A and 40-3908D that match the target areas3904A, 3906A, 3906D, 3908A and 3908D, which are substantially smallerthan their respective target regions 3904, 3906 and 3908, the matingsurface contact regions 3984, 3986 and 3988 may be smaller and/orsegmented as compared to what is depicted in FIG. 47C.

FIG. 47E depicts closed-loop contour lines 4002, 4004, and 4006 that areimage segmented from image slices, wherein the contour lines outline thecortical bone surface of the lower end of the femur. These contour lines4002, 4004, and 4006 may be identified via image segmentation techniquesfrom medical imaging slices generated via, e.g., MRI or CT.

As shown in FIG. 47E, there are posterior portions of the contour lines(indicated as 4007) that may be of no interest during overestimationbecause the contour line region 4007 corresponds to a region of the kneethat may be inaccessible during surgery and may not correspond to a jigsurface because no part of the jig may access the region 4007 duringsurgery. An osteophyte in contour line region 4008 may be identifiedbased on the algorithm 2500. The contour lines in region 4008 may besubsequently overestimated (based on the algorithm 2500) such that theresulting jig surface does not come into contact with the osteophyte(i.e., with the osteophyte bone surface represented by contour lineregion 4008) when the jig's bone mating surface 40 matingly receives andcontacts the bone surfaces of the arthroplasty target region.Additionally, optimal contour line regions 4010 and 4012 may beidentified during execution of the algorithm 2500 as areas of thepatient's bone anatomy that have surface variations within the angularcriteria of the algorithm 2500 and, therefore, are used to generate thejig's bone mating surface 40 that matingly receives and contacts thebone surfaces of the arthroplasty target region.

Contour line region 4010 may pertain to region 3904 of FIG. 47A andfemur jig region 40-3904 of FIG. 47B. Contour line region 4012 maypertain to either region 3906 or 3908 of FIG. 47A and either femur jigregion 40-3906 or 40-3908 of FIG. 47B. Utilizing the optimal areas 4010and 4012 as jig bone mating surfaces 40 allows irregular areas of thepatient's bone anatomy to be accommodated without affecting the fit ofthe jig 2 to the patient's bone anatomy. In fact, an accurate and customfit between the jig 2 and the patient's bone anatomy can be made byusing only a few of such optimal areas. This allows substantialoverestimation of the jig surface in regions corresponding toirregularities, thereby preventing the irregularities from interferingwith an accurate and reliable fit between the jig's bone mating surfacesand those bone surfaces of the arthroplasty target region correspondingto those bone mating surfaces. The result of the overestimation processis a jig with bone mating surfaces that offer a reliable and accuratecustom fit with the arthroplasty target region. This may result in anincreased success rate for TKR or partial knee replacement surgerybecause the jig may custom fit to the most reliable bone surfaces and bedeliberately spaced from the bone surfaces that may be unreliable, forexample, because of imaging or tool machinery limitations.

2. Overestimating the 3D Tibia Surface Models

As described above with regard to block 140 of FIG. 1D, the “jig data”46 is used to produce a jigs having bone mating surfaces customized tomatingly receive the target areas 42 of the respective bones of thepatent's joint. Data for the target areas 42 may be based, at least inpart, on the 3D computer generated surface models 40 of the patient'sjoint bones. Furthermore, as described above with regard to FIG. 1A and[blocks 100-105] of FIG. 1B, these 3D computer generated surface models40 may be based on the plurality of 2D scan image slices 16 taken fromthe imaging machine 8 and, more precisely, from the contour linesderived from those 2D scan image slices via image segmentation processesknown in the art or, alternatively, as disclosed in U.S. ProvisionalPatent Application 61/126,102, which was filed Apr. 30, 2008 and isincorporated by reference herein in its entirety.

Each scan image slice 16 represents a thin slice of the desired bones.FIG. 48A illustrates the proximal axial view of the 3D model of thepatient's tibia shown in FIG. 43I with the contour lines 4101 of theimage slices shown and spaced apart by the thickness D_(T) of theslices. FIG. 48B represents a coronal view of a 3D model of thepatient's tibia with the contour lines 4101 of the image slices shownand spaced apart by the thickness D_(T) of the slices.

The slices shown in FIGS. 48A-B have contour lines 4101 similar to theopen and closed loop contour line segments 210 y, 210 y′ depicted inFIGS. 41B and 41E. The contour lines 4101 of each respective image slice16 are compiled together to form the 3D model of the patient's tibia.The overall resolution or preciseness of the 3D models 40 (shown in FIG.43C) resulting from compiling together the contour lines of each ofthese slices (shown in [block 1010]) may be impacted by the thicknessD_(T) of the slices shown in FIGS. 48A-B. Specifically, the greater thethickness D_(T) of the slices, the lower the resolution/preciseness ofthe resulting 3D models, and the smaller the thickness D_(T) of theslices, the higher the resolution/preciseness of the resulting 3Dmodels.

As the resolution/preciseness of the 3D models increases, more accuratecustomized arthroplasty jigs 2 may be generated. Thus, the generalimpetus is to have thinner slices rather than thicker slices. However,depending upon the imaging technology used, the feasible thickness D_(T)of the image slices may vary and may be limited due a variety ofreasons. For example, an imaging thickness D_(T) that is sufficientlyprecise to provide the desired imaging resolution may also need to bebalanced with an imaging duration that is sufficiently brief to allow apatient to remain still for the entire imaging duration.

In embodiments utilizing MRI technology, the range of slice thicknessD_(T) may be from approximately 0.8 mm to approximately 5 mm. MRI slicethicknesses D_(T) below this range may be unfeasible because they haveassociated imaging durations that are too long for most patient's toremain still. Also, MRI slice thicknesses D_(T) below this range may beunfeasible because they may result in higher levels of noise with regardto actual signals present, residuals left between slices, and volumeaveraging limitations of the MRI machine. MRI slice thicknesses abovethis range may not provide sufficient image resolution/preciseness. Inone embodiment, the MRI slice thicknesses D_(T) is approximately 2 mm.

While embodiments utilizing CT technology may have a range of slicethicknesses D_(T) from approximately 0.3 mm to approximately 5 mm, CTimaging may not capture the cartilage present in the patient's joints togenerate the arthritic models mentioned above.

Regardless of the imaging technology used and the resultingresolution/preciseness of the 3D models, the CNC machine 10 may beincapable of producing the customized arthroplasty jigs 2 due tomechanical limitations, especially where irregularities in the bonesurface are present. This, for example, may result where a milling toolbit has dimensions that exceed those of the feature to be milled.

FIG. 48C illustrates an example sagittal view of compiled contour linesof successive sagittal 2D MRI images based on the slices shown in FIGS.48A-B with a slice thickness D_(T) of 2 mm. As can be understood fromFIGS. 48A-48C, the contour lines shown begin on the medial side of theknee at the image slice corresponding to contour line 4110 and concludeon the lateral side of the knee at the image slice corresponding tocontour line 4130. Thus, in one embodiment, contour lines 4110 and 4130represent the contour lines of the first and last images slices taken ofthe tibia, with the other contour lines between contour lines 4110, 4130representing the contour lines of the intermediate image slices taken ofthe tibia. Each of the contour lines is unique is size and shape, may beeither open-loop or closed-loop, and corresponds to a unique image slice16.

FIG. 48D illustrates an example contour line 4300 of one of the contourlines depicted in FIGS. 48A-48C, wherein the contour line 4300 isdepicted in a sagittal view and is associated with an image slice 16 ofthe tibia plateau. As shown, the contour line 2400 includes a pluralityof surface coordinate points (e.g., i.e., i−n, . . . , i−3, i−2, i−1, i,i+1, i+2, 1+3, . . . , i+n; j−n, . . . , j−3, j−2, j−1, j, j+1, j+2,j+3, . . . , j+n; and k−n, . . . , k−3, k−2, k−1, k, k+1, k+2, k+3, . .. , k+n). The contour line and associated points may be generated byimaging technology, for example, via an image segmentation process thatmay employ, for example, a shape recognition process and/or an pixelintensity recognition process. In one embodiment, the contour line 4300may represent the boundary line along the cortical-cancellous bone edge.In one embodiment, the boundary line may represent the outer boundaryline of the cartilage surface.

Each of the surface contour points in the plurality may be separated bya distance “d”. In one embodiment, distance “d” may be a function of theminimum imaging resolution. In some embodiments, distance “d” may befunction of, or associated with, the size of the milling tool used tomanufacture the jig. For example, the distance “d” may be set to beapproximately 10 times smaller than the diameter of the milling tool. Inother words, the distance “d” may be set to be approximately 1/10^(th)or less of the diameter of the milling tool. In other embodiments, thedistance “d” may be in the range of between approximately equal to thediameter of the milling tool to approximately 1/100^(th) or less of thediameter of the milling tool.

Depending on the embodiment, the separation distanced may be eitheruniform along the contour line 4300, or may be non-uniform. For example,in some embodiments, areas of bone irregularities may have points thatare closer together than areas where no irregularities are present. Inone embodiment, the points shown along the example contour line 4300 mayhave a separation distance d of approximately 2 mm. In otherembodiments, distance d may be in the range of approximately 0.8 mm toapproximately 5 mm.

The bone surface of the example contour line 4300 includes a region4302A on the anterior portion of the tibia plateau, a region 4302B onthe tibia plateau that is representative of an irregularity, and aregion 4302C on the articular surface of the tibia plateau. Theirregularity of region 4302B may be due to a variety of patient specificfactors. For example, irregular region 4302B illustrates a type of boneirregularity, referred to as an “osteophyte”, where a bony outgrowth hasoccurred in the tibia plateau. Osteophytes may be present in patientsthat have undergone trauma to the bone or who have experienceddegenerative joint disease.

Irregularities may be due to other factors, such as cartilage damage,which may appear as notches in the contour line 4300. Regardless of thecause of the irregularities, the presence of irregularities in thecontour line 4300 may adversely impact the ability to generate a matingsurface in the actual arthroplasty jig that accurately and reliablymates with the corresponding bone surface of the patient during thearthroplasty procedure. This may be the result of the imagingimpreciseness in the vicinity of the contour irregular region 4302B orbecause the contour irregular region 4302B represents a surface contourthat is too small for the tooling of the CNC machine 10 to generate. Toaccount for contour line regions associated with imaging imprecisenessand/or features too small to be milled via the tooling of the CNCmachine, in some embodiments, such contour line regions may beidentified and corrected or adjusted via the overestimation processprior to being compiled to form the 3D models 40.

As discussed above, FIG. 45E represents an example overestimationalgorithm 2500 that may be used to identify and adjust for irregularregion 4302B when forming the 3D models 40. In block 2502, medicalimaging may be performed on the damaged bone at desired slicethicknesses D_(T), which in some embodiments may be equal to those slicethicknesses D_(T) mentioned above with regard to FIGS. 48A-B. Forexample, MRI and/or CT scans may be performed at predeterminedthicknesses D_(T) as shown in FIGS. 48A-B. In some embodiments, thedesired thickness D_(T) used in block 2502 is set at 2 mm or any otherthickness D_(T) within the range of thicknesses D_(T) mentioned above.

From this medical imaging, a series of slices 16 may be produced andimage segmentation processes can be used to generate the contour lines210 y, 210 y′, 4101, 4110, 4130, 4300 discussed with respect to FIGS. 2,41A-B, 48A-B, and 43 (see block 2504). Also in block 2504, a pluralityof surface coordinate points along each contour line segment 4302A-C maybe identified as shown in FIG. 48D with respect to contour line 4300.For example, the points in the irregular region corresponding to contourline segment 4302B may be identified and indexed as k−n, . . . , k−3,k−2, k−1, k, k+1, k+2, k+3, . . . , k+n.

With the surface coordinate points along the contour 4300 defined, ananalysis may be performed on two or more of the points (e.g., k and k+1)to determine if an irregularity exists in the contour line segment perblock 2506.

FIG. 48E depicts implementing an example analysis scheme (according toblock 2506) on the irregular contour line region 4302B of FIG. 48D. Asshown, the analysis may include constructing one or more tangent lines(labeled as t_(k−1) t_(k), t_(k+1), t_(k+2), t_(k+3), t_(k+4), etc.),corresponding to the points in the irregular region 4302B. The analysisof block 2506 may further include calculating differences between theangles formed by one or more of the tangent lines. For example, thedifference between the angles formed by the tangent lines t_(k) andt_(k+1) may be defined as w_(k), where

$w_{k} = {{\cos^{- 1}\left( \frac{t_{k + 1} \cdot t_{k}}{{t_{k + 1}}{t_{k}}} \right)}.}$In some embodiments, the operations of block 2506 may be performedrepetitively on each point within the contour segment.

The operations of block 2506 may be calculated on subsequent points(e.g., between t_(k) and t_(k+1)) in some embodiments, and onnon-subsequent points in other embodiments (e.g., t_(k+2) and t_(k+4)).

The angular difference w may indicate whether portions of the contourline segment are too eccentric for use in constructing the 3D models 40.In block 2508, the angular difference w may be compared to apredetermined angular criterion w_(c). The angular criterion w_(c) maybe determined based on several factors, including the physicaldimensions and characteristics of the CNC machine 10. In someembodiments, the predetermined angular criterion w_(c) is set atapproximately 5 degrees. In other embodiments, the predetermined angularcriterion w_(c) is set at between approximately 5 degrees andapproximately 20 degrees.

For the sake of discussing the example irregular region 4302B shown inFIG. 48E, the angular criterion w_(c) is set to 5 degrees in oneembodiment. The angular differences between tangent lines associatedwith adjacent points k−4, k−3, k−2 and k+12, k+13, and k+14 are withinthe predetermined angular criterion w_(c) of 5 degrees, but thedifferences between tangent lines associated with adjacent points k−3,k−2, k−1, ki, k+1, k+2, . . . , k+10 exceeds the predetermined angularcriterion w_(c) of 5 degrees and therefore indicates an irregular regionof the contour line. As mentioned above, these irregularities may resultfrom conditions of the patient's bone such as arthritis orosteoarthritis and generally result in a contour line segment beingunsuitable for using when forming the 3D models 40. Accordingly, if thecomparison from block 2508 indicates that the angular difference w isgreater than the predetermined criterion w_(c), then the data associatedwith the irregular contour line segment may be modified byoverestimating (e.g., adjusting the irregular contour line segmentoutward or away from the bone portion of the image slice 16) asdiscussed in greater detail below with respect to FIG. 48F (see block2510).

FIG. 48F depicts the irregular region 4302B from FIG. 48E including aproposed area of overestimation 4501, wherein an overestimationprocedure creates an adjusted contour line 4502 and positionallydeviates the adjusted contour line 4502 from the original surfaceprofile contour line 4302B. In the event that the comparison performedin block 2508 indicates that the angular differences between any of thepoints k-3 through k+10 exceed the predetermined angular criterionw_(c), then the contour line segment may be overestimated between thesepoints as shown by the dashed line 4502. As can be understood from acomparison of contour line 4302B to the overestimated or adjusted line4502, the adjusted line 4502 is adjusted or moved outward or away fromthe location of the contour line 4502B by an offset distance. Dependingon the embodiment, the offset distance between the contour line 4302Band the adjusted line 4502 may range between a few millimeters to a fewcentimeters. This overestimation may be built into the data used toconstruct 3D surface models 40 and result in a gap between therespective region of the bone mating surface of the jig 2 and thecorresponding portion of the patient's bone surface, thereby avoidingcontact between these respective areas of the jig and bone surface. Theother areas, such as k−6, k−7, k−8, k−9 and k+15, k+16, k+17, and k+18,need not be overestimated, per block 2510, because the differencesbetween their tangent lines fall within the angular difference criterionw_(c). These areas may be designated as potential target areas that maylater be used as the 3D surface models 40 if other angular criteria(described below) are satisfied.

By building overestimation data into the 3D surface models 40,deliberate spaces may be created in regions of the custom arthroplastyjig 2 corresponding to irregularities in the patient's bone, where it isoften difficult to predict the size and shape of these irregularitiesfrom 2D MRI or where it is difficult to accurately machine the contourline into the jig's bone mating surface because of the largeness of themilling tool relative to the changes in contour. Thus, the jig 2 mayinclude one or more deliberate spaces to accommodate theseirregularities or inability to machine. Without these deliberate spaces,the jig 2 may be potentially misaligned during the TKR surgery and mayreduce the chances of the surgery's success.

As described above with respect to FIGS. 45H and 45L, the imagegeneration, analysis and overestimation of blocks 2506, 2508 and 2510may be performed on other irregularities of contour line 4300, if suchadditional irregularities were present in FIG. 48D.

As shown in FIG. 45, a tool 4504 having diameter D₂ may be employed tomachine the contour line 4302 into the jig blank. As described abovewith respect to FIG. 45I, in some embodiments, to allow for an adequatetransition from the non-overestimated regions to the overestimatedregions 4501 in view of the diameter D₂ of the tool 4504 to be used, theoverestimation may include additional points to either side of thepoints falling outside of the predetermined criterion w_(c) (i.e.,points k−3, k−4, and k−5 as well as at points k+12, k+13, and k+14).Thus, the overestimation in region 4302B may extend from k−5 throughk+14. Furthermore, since the comparison performed in block 2508indicates that the angular difference w_(k) is less than thepredetermined criterion w_(c) at points k−3, k−4, k−5, k−6, k−7, k−8,k−9 and k+12, k+13, k+14, k+15, k+16, k+17, and k+18, these points k−6,k−7, k−8, k−9 and k+15, k+16, k+17, and k+18 (adjusting for the additionof points k−3, k−4, and k−5 as well as at points k+12, k+13 to theoverestimation transition regions 4501) may be used in constructing the3D models 40 as long as other criteria (described below in the contextof blocks 2514-2520) are met.

A tool 4504 may be used to form the surface of the jig's bone matingsurface from the 3D models 40 formed from the compiled contour lines,some of which may have been modified via the overestimation process. Thetool 4504 may be part of the CNC machine 10 or any other type ofmachining or manufacturing device having any type of tool or device forforming a surface in a jig blank. Regardless of the type of the deviceused to mill or form the jigs 2, the tool 4504 may have certainattributes associated with jig machining process that are taken intoaccount when performing the overestimating per block 2510. Theassociated attributes may include the accessible space for the machiningtools to reach and machine the jig's bone mating surface. Examples ofsuch attributes may include the collar diameter of the drilling cutterdevice, the allowable angle the drilling device can make with thesurface to be drilled (e.g., 45 degrees±10%), and/or the overall lengthof the drilling cutter head.

For example, as indicated in FIG. 45, if the minimum diameter of theoverestimated region 4501 is larger than the diameter D₂ of the tool4504, then overestimation of block 2510 may not need to account for thedimensions of the tool 4504, except to provide adequate transitionsleading to the overestimated region 4501 as illustrated above by theaddition of a single or few points (e.g., points k−3, k−4, and k−5 aswell as at points k+12, k+13) to either side of the points outsidepredetermined criterion w_(c).

If, on the other hand, the tool 4504 has a diameter D₂ that is greaterthan the diameter of the overestimated region, then the overestimatedregion may be increased in diameter to account for the large tooldiameter, as described above with respect to FIGS. 45J-45K. With thecurves overestimated to account for factors related to the tool 4504,the resulting overestimated surface profile or contour may be saved forgenerating the 3D model 40 as long as other criteria (described below inthe context of block 2514-2520) are met.

FIGS. 48G-H show similar analyses of the regular regions 4302A and 4302C(from FIG. 43). As was the case with the irregular region 4302B, pointsi+1, i+2, i+3, i+n and j+1, j+2, j+3, j+n along the contour line 4300may be identified for regions 4302A and 4302C and then tangent lines(labeled as t_(j+1), t_(j+2), t_(j+3), etc. and t_(i+1), t_(i+2),t_(i+3), etc.) may be constructed per block 2506. Per block 2508,comparing the angular differences w between these tangent lines usingthe formulas

$w_{j} = {{{\cos^{- 1}\left( \frac{t_{j + 1} \cdot t_{j}}{{t_{j + 1}}{t_{j}}} \right)}\mspace{14mu}{and}\mspace{14mu} w_{i}} = {\cos^{- 1}\left( \frac{t_{i + 1} \cdot t_{i}}{{t_{i + 1}}{t_{i}}} \right)}}$shows that they w_(j), w_(i) are within the angular criterion w_(c),which in this example is 5 degrees. Thus, the points of the regions4302A and 4302C shown in FIGS. 48G-H may be saved and used as potentialsurface profiles for the mating surface of the tibial jig if the surfacevariations between these points and points on contour lines of adjacentslices are not too extreme. That is, if the angular differencesassociated with a contour line of a particular slice fall within theangular criterion w_(c), and the points are used as a potential jigsurface, then surface variation between contour lines of adjacent slicesmay be checked in block 2514. This approach may help to identify certainareas where no cartilage damage or osteophyte is observed in theimaging, yet there is a need to overestimate because the surfacevariation, between the adjacent slices shown in FIGS. 48A-B, may be toogreat to be used as an accurate representation of the actual bonesurface to be a potential tibial jig surface. Example areas fallingwithin this category for the proximal tibia plateau include the areasnear the medial and lateral tibial plateaus adjacent to, and including,the spine portion to name a few examples.

Once it is determined that a specific portion of a contour line hassatisfied the criterion w_(c) of block 2508 of FIG. 45E, that contourline portion may be further analyzed to determine if the contour lineportion also satisfies both of the criterion θ_(c) and φ_(c) of block2514, as discussed above with respect to FIGS. 45E and 45N-46B. Morespecifically, corresponding coordinate points are determined via any ofthe three methods discussed above with respect to FIGS. 46A-46F. Thesurface variation between the corresponding coordinate points isanalyzed as discussed with above with respect to FIGS. 46A-46F withrespect to: (1) angular deviation θ between corresponding coordinatepoints of contour lines of adjacent image slices; and (2) the angulardifferences φ of normal vectors associated with corresponding coordinatepoints of contour lines of adjacent image slices. If the contour lineportion meets all of the criterion w_(i), θ_(c) and φ_(c) of blocks 2508and 2514 of FIG. 45E, then, as discussed above and indicated in block2520 of FIG. 45E, the contour line portion may be recorded and employedin generating the jig's bone mating surfaces. Alternatively, if thecontour portion line fails to meet any one or more of the criterionw_(i), θ_(c) and φ_(c) of blocks 2508 and 2514, then as indicated inFIG. 45E and discussed above, the contour line portion may be modifiedper the overestimation process (block 2510) or, in some instances, theimage slice thickness D_(T) may be reset to a more narrow thicknessD_(T) and the entire process repeated beginning at block 2502 of FIG.45E.

FIG. 48I is a proximal view of the tibia plateau similar to that of FIG.43I depicting contour lines 4700 produced by imaging the left tibia atan image spacing D_(T) of, for example, 2 mm. As shown, the contourlines 4700 may be grouped into multiple regions in the lateral-medialdirection 4702-4708 for the sake of discussion. The region 4702 includesthe contour lines 4700 of the most medial half of the medial tibialplateau and extends laterally from the most medial side of the medialtibial plateau to the medial-lateral middle of the medial tibialplateau. The region 4704 includes the contour lines 4700 of the mostlateral half of the medial tibial plateau and extends laterally from themiddle of the medial tibial plateau to the medial-lateral point near thetibial spine. The region 4706 includes the contour lines 4700 of themost medial half of the lateral tibial plateau and extends laterallyfrom the medial-lateral point near the tibial spine to themedial-lateral middle of the lateral tibial plateau. The region 4708includes the contour lines 4700 of the most lateral half of the lateraltibial plateau and extends laterally from the medial-lateral middle ofthe lateral tibial plateau to the most lateral side of the lateraltibial plateau.

FIG. 48J is a sagittal view of the contour lines 4700 of region 4702 ofFIG. 48I. The contour lines 4700 of region 4702 include contour lines4802-4812, with the most medial portion of the medial tibial plateaubeing indicated by contour line 4802. The size of each successivecontour line 4700 of region 4702 increases moving laterally from themost medial contour line 4802 of region 4702 to the most lateral contourline 4812 of region 4702, which is near the medial-lateral middle of themedial tibial plateau.

As can be understood from FIG. 48J, the contour lines 4802-4803 arespaced apart from their respective adjacent contour lines a substantialamount around their entire boarders. Such wide spacing corresponds to asubstantial amount of rise or fall distances between adjacent contourlines, as discussed above with respect to FIG. 46B. Thus, such contourlines would likely fail to meet the angular criterion θ_(c) and besubject to the overestimation process such that jig surfacescorresponding to the contour lines 4802-4803 would not contact thecorresponding surfaces of the arthroplasty target areas.

As can be understood from FIG. 48J, in the proximal portion of themedial tibial plateau, the contour lines 4804-4812 in the region 4814converge such that there is little, if any, amount of rise or falldistance between adjacent contour lines. Thus, such contour lines4804-4812 in the region 4814 would likely meet the first angularcriterion θ_(c). Similarly, in the anterior tibial plateau portion ofthe proximal tibia, the contour lines 4811-4812 in region 4816 convergesuch that there is little, if any, amount of rise or fall distancebetween adjacent contour lines. Thus, such contour lines 4804-4812 inregion 4814 and contour lines 4811-4812 in region 4816 would likely meetthe first angular criterion θ_(c).

As can be understood from the arrows in regions 4814 and 4816, theangular differences between normal vectors for the contour line portionswithin regions 4814 and 4816 would be minimal, likely meeting the secondangular criterion φ_(c). Thus, as the portions of the contour lines4804-4812 within region 4814 and the portions of the contour lines4811-4812 within region 4816 likely meet both angular criterion θ_(c)and φ_(c), the portions of the contour lines 4804-4812 within the region4814 and the portions of the contour lines 4811-4812 within region 4816represent optimal contact areas 4814 and 4816 for mating contact withthe jig's bone mating surface 40.

In one embodiment, as can be understood from FIG. 49A discussed below,the optimal contact area 4814 may be the surface of the medial tibialplateau that displaces against the corresponding articular surface ofthe medial femoral condyle, and the optimal contact area 4816 may be themedial anterior region of the proximal tibia just distal of the tibialplateau edge and medial of the tuberosity of the tibia.

In one embodiment, the optimal contact areas 4814 and 4816 matinglycorresponds to the jig's bone mating surface 40 such that the portionsof the contour lines 4702 indicated by regions 4814 and 4816 may be usedto generate the jig's bone mating surface 40, per the algorithm 2500 ofFIG. 45E. Conversely, per the algorithm 2500, the portions of thecontour lines 4702 outside regions 4814 and 4816 may be subjected to theoverestimation process discussed above such that the jig's surfacescreated from the overestimated contour line portions results in jigsurfaces that do not contact the corresponding portions of the patient'sarthroplasty target regions.

FIG. 48K is a sagittal view of the contour lines 4700 of region 4704 ofFIG. 48I. The contour lines 4700 of region 4704 include contour lines4902, 4903, 4904, 4905, 4906, 4907, 4908, 4909 and 4910 with the mostmedial portion of region 4704 being indicated by contour line 4802,which is near the medial-lateral middle of the medial tibial plateau,and the most lateral portion of region 4704 being indicated by contourline 4810, which is a medial-lateral point near the tibial spine. Thesize of each successive contour line 4700 of region 4704 increasesmoving laterally from the most medial contour line 4902 to the mostlateral contour line 4910.

As can be understood from FIG. 48K, the contour lines 4902-4910 arespaced apart from their respective adjacent contour lines a substantialamount in their posterior and anterior portions along the shaft of thetibia, and to a lesser extent in their tibia spine portions. Such widespacing corresponds to a substantial amount of rise or fall distancesbetween adjacent contour lines, as discussed above with respect to FIG.46B. Thus, such contour lines would likely fail to meet the angularcriterion θ_(c) and be subject to the overestimation process such thatjig surfaces corresponding to the contour lines 4902-4910 would notcontact the corresponding surfaces of the arthroplasty target areas.

As can be understood from FIG. 48K, in the anterior tibial plateauportion of the proximal tibia, the contour lines 4902-4910 in the region4912 converge such that there is little, if any, amount of rise or falldistance between adjacent contour lines. Thus, such contour lines4902-4910 in the region 4912 would likely meet the first angularcriterion θ_(c).

As can be understood from the arrows in region 4912, the angulardifferences between normal vectors for the contour line portions withinthe region 4912 would be minimal, likely meeting the second angularcriterion φ_(c). Thus, as the portions of the contour lines 4902-4910within region 4912 likely meet both angular criterion θ_(c) and φ_(c),the portions of the contour lines 4902-4910 within the region 4912represent an optimal contact area 4912 for mating contact with the jig'sbone mating surface 40.

In one embodiment, the optimal contact area 4912 matingly corresponds tothe jig's bone mating surface 40 such that the portions of the contourlines 4902-4910 indicated by region 4912 may be used to generate thejig's bone mating surface 40, per the algorithm 2500 of FIG. 45E.Conversely, per the algorithm 2500, the portions of the contour lines4902-4910 outside region 4912 may be subjected to the overestimationprocess discussed above such that the jig's surfaces created from theoverestimated contour line portions results in jig surfaces that do notcontact the corresponding portions of the patient's arthroplasty targetregions.

In one embodiment, as can be understood from FIG. 49A discussed below,the optimal contact area 4912 may be the anterior region of the proximaltibia just distal of the tibial plateau edge and just distal of thetuberosity of the tibia, extending medial-lateral from just medial ofthe tuberosity of the tibia to generally centered medial-lateralrelative to the tuberosity of the tibia.

FIG. 48L is a sagittal view of the contour lines 4700 of region 4706 ofFIG. 48I. The contour lines 4700 of region 4706 include contour lines5002, 5003, 5004, 5005, 5006, 5007, 5008, 5009 and 5010 with the mostmedial portion of region 4706 being indicated by contour line 5002,which is a medial-lateral point near the tibial spine, and the mostlateral portion of region 4704 being indicated by contour line 5010,which is near the medial-lateral middle of the lateral tibial plateau.The size of each successive contour line 4700 of region 4704 decreasesmoving laterally from the most medial contour line 5002 to the mostlateral contour line 5010.

As can be understood from FIG. 48L, the contour lines 5002-5010 arespaced apart from their respective adjacent contour lines a substantialamount in their posterior and anterior portions along the shaft of thetibia, and to a lesser extent in their tibia spine and tibia tuberosityportions. Such wide spacing corresponds to a substantial amount of riseor fall distances between adjacent contour lines, as discussed abovewith respect to FIG. 46B. Thus, such contour lines would likely fail tomeet the angular criterion θ_(c) and be subject to the overestimationprocess such that jig surfaces corresponding to the contour lines5002-5010 would not contact the corresponding surfaces of thearthroplasty target areas.

As can be understood from FIG. 48L, in the anterior tibial plateauportion of the proximal tibia, the contour lines 5002-5010 in the region5012 converge such that there is little, if any, amount of rise or falldistance between adjacent contour lines. Thus, such contour lines5002-5010 in the region 5012 would likely meet the first angularcriterion θ_(c).

As can be understood from the arrows in region 5012, the angulardifferences between normal vectors for the contour line portions withinthe region 5012 would be minimal, likely meeting the second angularcriterion φ_(c). Thus, as the portions of the contour lines 5002-5010within region 5012 likely meet both angular criterion θ_(c) and φ_(c),the portions of the contour lines 5002-5010 within the region 5012represent an optimal contact area 5012 for mating contact with the jig'sbone mating surface 40.

In one embodiment, the optimal contact area 5012 matingly corresponds tothe jig's bone mating surface 40 such that the portions of the contourlines 5002-5010 indicated by region 5012 may be used to generate thejig's bone mating surface 40, per the algorithm 2500 of FIG. 45E.Conversely, per the algorithm 2500, the portions of the contour lines5002-5010 outside region 5012 may be subjected to the overestimationprocess discussed above such that the jig's surfaces created from theoverestimated contour line portions results in jig surfaces that do notcontact the corresponding portions of the patient's arthroplasty targetregions.

In one embodiment, as can be understood from FIG. 49A discussed below,the optimal contact area 5012 may be the anterior region of the proximaltibia just distal of the tibial plateau edge and just distal of thetuberosity of the tibia, extending medial-lateral from just lateral ofthe tuberosity of the tibia to generally centered medial-lateralrelative to the tuberosity of the tibia.

FIG. 48M is a sagittal view of the contour lines 4700 of region 4708 ofFIG. 48I. The contour lines 4700 of region 4708 include contour lines5102-5112, with the most lateral portion of the lateral tibial plateaubeing indicated by contour line 5102. The size of each successivecontour line 4700 of region 4708 increases moving laterally from themost medial contour line 5102 of region 4708, which is near themedial-lateral middle of the medial tibial plateau, to the most lateralcontour line 5110 of region 4708, which is the most lateral portion ofthe lateral tibial plateau.

As can be understood from FIG. 48M, the contour lines 5110-5112 arespaced apart from their respective adjacent contour lines a substantialamount around their entire boarders. Such wide spacing corresponds to asubstantial amount of rise or fall distances between adjacent contourlines, as discussed above with respect to FIG. 46B. Thus, such contourlines would likely fail to meet the angular criterion θ_(c) and besubject to the overestimation process such that jig surfacescorresponding to the contour lines 5110-5112 would not contact thecorresponding surfaces of the arthroplasty target areas.

As can be understood from FIG. 48M, in the proximal portion of thelateral tibial plateau, the contour lines 5102-5109 in the region 5114converge such that there is little, if any, amount of rise or falldistance between adjacent contour lines. Thus, such contour lines5102-5109 in the region 5114 would likely meet the first angularcriterion θ_(c). Similarly, in the anterior tibial plateau portion ofthe proximal tibia, the contour lines 5102-5105 in region 5116 convergesuch that there is little, if any, amount of rise or fall distancebetween adjacent contour lines. Thus, such contour lines 5102-5109 inregion 5114 and contour lines 5102-5105 in region 5116 would likely meetthe first angular criterion θ_(c).

As can be understood from the arrows in regions 5114 and 5116, theangular differences between normal vectors for the contour line portionswithin regions 5114 and 5116 would be minimal, likely meeting the secondangular criterion φ_(c). Thus, as the portions of the contour lines5102-5109 within region 5114 and the portions of the contour lines5102-5105 within region 4816 likely meet both angular criterion θ_(c)and φ_(c), the portions of the contour lines 5102-5109 within the region5114 and the portions of the contour lines 5102-5105 within region 5116represent optimal contact areas 5114 and 5116 for mating contact withthe jig's bone mating surface 40.

In one embodiment, as can be understood from FIG. 49A discussed below,the optimal contact area 5114 may be the surface of the lateral tibialplateau that displaces against the corresponding articular surface ofthe lateral femoral condyle, and the optimal contact area 5116 may bethe lateral anterior region of the proximal tibia just distal of thetibial plateau edge and lateral of the tuberosity of the tibia.

In one embodiment, the optimal contact areas 5114 and 5116 matinglycorresponds to the jig's bone mating surface 40 such that the portionsof the contour lines 4708 indicated by regions 5114 and 5116 may be usedto generate the jig's bone mating surface 40, per the algorithm 2500 ofFIG. 45E. Conversely, per the algorithm 2500, the portions of thecontour lines 4708 outside regions 5114 and 5116 may be subjected to theoverestimation process discussed above such that the jig's surfacescreated from the overestimated contour line portions results in jigsurfaces that do not contact the corresponding portions of the patient'sarthroplasty target regions.

As can be understood from the preceding discussion, the overestimationprocess disclosed herein can be used to identifying optimal target areas(e.g., optimal target areas 4814, 4816, 4912, 5012, 5114, 5116 asdiscussed with respect to FIGS. 48I-48M). More specifically, theoverestimation process disclosed herein can employ these optimal targetareas to generate the bone mating surfaces 40 of the jigs 2 whilecausing the other surface areas of the jigs to be configured such thatthese other jig surface areas will not contact the surfaces of thearthroplasty target areas when the jig's bone mating surfaces 40 havematingly received and contacted the arthroplasty target areas. Theresult is a jig that has bone mating surfaces 40 that are based on theregions of the arthroplasty target region that are most accuratelyrepresented via 3D computer modeling and most likely to be machinableinto the jig. Such a jig provides an increased accuracy of fit betweenthe jig's mating surface 40 and the arthroplasty target areas of thepatient's bone.

For most patients, it is common that the overestimation process outlinedin FIG. 45E will result in certain areas of the tibial arthroplastytarget region being identified as the optimal target areas discussedabove with respect to FIGS. 48I-48M. For example, as depicted in FIG.49A, which is proximal-sagittal isometric view of a tibial proximal end5200, a commonly encountered, healthy, non-deformed tibial proximal end5200 may have an arthroplasty target region 5202 with certain optimaltarget regions 5204, 5206 and 5208. These optimal target regions 5204,5206 and 5208 commonly identified on most patients via theoverestimation process of FIG. 45E are indicated in FIG. 49A by thecross-hatched regions. It has been found that these optimal targetregions 5204, 5206 and 5208 are the regions of the arthroplasty targetregion 5202 that are most likely to satisfy the criterion w_(i), θ_(c)and φ_(c) of blocks 2508 and 2514 of FIG. 45E. Therefore, these targetregions 5204, 5206 and 5208 may be used to generate the jig's bonemating surfaces 40.

While, in one embodiment, the overestimation process of FIG. 45E islikely to result in optimal target regions such as those indicated viathe cross-hatching regions 5204, 5206 and 5208, in other embodiments,the optimal target regions may result in target regions in otherlocations on the tibial proximal end 5200 that are in addition to, or inplace of, those regions 5204, 5206 and 5208 depicted in FIG. 49A.

One of the benefits of the overestimation process of FIG. 45E is that itidentifies two types of contour lines 210 y, the first type being thosecontour lines that are most likely to be unacceptable for the generationa jig's bone mating surfaces 40, and the second type being those contourlines that are most likely to be acceptable for the generation of ajig's bone mating surfaces 40. The first type of contour lines areunlikely to be acceptable for the generation of a jig's bone matingsurfaces 40 because they pertain to bone surfaces that are too varied tobe accurately 3D computer modeled and/or are such that they are notreadily machinable into the jig blank. Conversely, the second type ofcontour lines are likely to be acceptable for the generation of a jig'sbone mating surfaces 40 because they pertain to bone surfaces that varysuch an insubstantial amount that they can be accurately 3D computermodeled and are such that they are readily machinable into the jigblank. While optimal target regions 5204, 5206 and 5208 representregions likely corresponding to contour lines of the second type formost commonly encountered patients, the overestimation processesdisclosed herein may be adapted to result in other or additional optimaltarget regions.

In some instances the entirety of the target regions 5204, 5206 and 5208may correspond to the second type of contour lines, namely those type ofcontour lines that satisfy the criterion w_(i), θ_(c) and φ_(c) ofblocks 2508 and 2514 of FIG. 45E. In such instances, the entirety of thetarget regions 5204, 5206 and 5208 are matingly contacted by the jig'sbone mating surface 40.

However, in some instances one or more potions of one or more of thetarget regions 5204, 5206 and 5208 may be subjected to overestimation sothat the jig's bone mating surface 40 does not contact such portions ofthe target regions 5204, 5206 and 5208, although the jig's bone matingsurface 40 still matingly contacts the other portions of the targetregions 5204, 5206 and 5208 corresponding to the second type of contourlines. Such a situation may arise, for example, where a substantialsurface variation (e.g., a hole, deformity or osteophyte) exists on atibial plateau articular surface 5218, 5219 that meets the criterionw_(i), θ_(c) and φ_(c) of blocks 2508 and 2514 for the rest of itssurface.

The overestimation process disclosed herein may result in theidentification of target regions 5204, 5206 and 5208 that are mostlikely to result in bone mating surfaces 40 of jigs 2 that are readilymachinable into the jigs 2 and most likely to facilitate reliable andaccurate mating of the jigs to the arthroplasty target regions. Theoverestimation process results in such accurate and reliable bone matingsurfaces 40 while causing other surfaces of the jigs 2 corresponding toless predictable bone surfaces to not contact the bone surfaces when thebone mating surfaces 40 matingly receive the target regions 5204, 5206and 5208 of the actual arthroplasty target region.

As indicated in FIG. 49A by the cross-hatched regions, optimal targetregions 5204, 5206 and 5208 may include three general areas of thetibial plateau 5210. For example, the anterior optimal target region5204 may include the anterior portion of the tibial proximal end 5200just distal of the anterior edge 5212 of the tibia plateau 5210 and justproximal of the tibial tuberosity 5214, the anterior optimal targetregion 5204 extending both medial and lateral of the tuberosity. Also,for example, the medial optimal target region 5206 may include thearticular portion of the medial tibial plateau 5220 (i.e., the portionof the medial tibial plateau 5224 that articulates against thearticulate surface of the medial femoral condyle), and the lateraloptimal target region 5208 may include the articular portion of thelateral tibial plateau 5222 (i.e., the portion of the lateral tibialplateau 5226 that articulates against the articulate surface of thelateral femoral condyle).

As indicated in FIG. 49A, the tibial proximal end 5200 may include amedial tibial plateau 5224, a lateral tibial plateau 5226, a tibialspine 5228 separating the two plateaus 5224, 5226, a tibial tuberosity5214, and a tibial shaft 5230 extending distally from the tibial plateauregion 5210. Each plateau 5224 and 5226 includes an articular surface5220 and 5222 that articulates against corresponding articular surfacesof the femoral condyles.

As indicated in FIG. 49E, which is a coronal view of the anterior sideof the tibial proximal end 5200, the medial tibial plateau 5224 andlateral tibial plateau 5226 converge to form the tibial spine 5228,which separates the two plateaus 5224, 5226 and forms the intercondyloideminence 52E1. The tibial shaft 5230 distally extends from the tibialplateau region 5210, and the tibial tuberosity 5214 is located on aproximal region of the shaft 5230. The lateral meniscus is indicated at52E2, the capsule is indicated at the dashed line at 52E3, the lateralcondyle is located at 52E4, the biceps and the anterior tibio-fibularligament are indicated at 52E5, the fibular lateral ligament isindicated at 52E6, the lateral digitorum longus is indicated at 52E7,the lateral surface of the tibia shaft or tibialis anterior is indicatedat 52E17, the semitendinosus is indicated at 52E8, the sartorius isindicated at 52E9, the graoilis is indicated at 52E10, the distalportion of the ligamentum patella is indicated at 52E11, the tibiallateral ligament is indicated at 52E12, the medial condyle is indicatedat 52E13, the anterior crucial ligament is indicated at 52E14, thecoronary ligament is indicated at 52E15, and the medial meniscus isindicated at 52E16.

As indicated in FIG. 49A by the cross-hatching, in one embodiment, themedial optimal target region 5206 may be generally coextensive with themedial articular surface 5220 that articulates against the respectivearticulate surface of the medial femoral condyle. In one embodiment, themedial optimal target region 5220 may extend: anterior-posterior betweenthe anterior edge 5240 and posterior edge 5242 of the medial tibialplateau 5224; and lateral-medial between the medial side 5446 of themedial tibial plateau 5224 and the medial base 5248 of the medial tibialspine. In one embodiment as can be understood from FIG. 49A, the medialoptimal target region 5206 may be the entire cross-hatched region 5206or any one or more portions of the cross-hatched region 5206.

In one embodiment as indicated in FIG. 49A by the double cross-hatching,a medial target area 5206A may be identified within the medial optimaltarget region 5206 via the overestimation process disclosed herein.Thus, although the medial optimal target region 5206 may be generallycoextensive with the medial articular surface 5220, the actual areawithin the medial optimal target region 5206 identified as being areliable surface for the generation of the mating surfaces ofarthroplasty jigs may be limited to a medial target area 5206A, theremainder of the medial optimal target region 5206 being subjected tothe overestimation process. The medial target area 5206A may be locatednear a central portion of the optimal target region 5206.

As indicated in FIG. 49A by the cross-hatching, in one embodiment, thelateral optimal target region 5208 may be generally coextensive with thelateral articular surface 5222 that articulates against the respectivearticulate surface of the lateral femoral condyle. In one embodiment,the lateral optimal target region 5222 may extend: anterior-posteriorbetween the anterior edge 5250 and posterior edge 5252 of the lateraltibial plateau 5226; and lateral-medial between the lateral side 5256 ofthe lateral tibial plateau 5226 and the lateral base 5258 of the lateraltibial spine. In one embodiment as can be understood from FIG. 49A, thelateral optimal target region 5208 may be the entire cross-hatchedregion 5208 or any one or more portions of the cross-hatched region5208.

In one embodiment as indicated in FIG. 49A by the double cross-hatching,a lateral target area 5208A may be identified within the lateral optimaltarget region 5208 via the overestimation process disclosed herein.Thus, although the lateral optimal target region 5208 may be generallycoextensive with the lateral articular surface 5222, the actual areawithin the lateral optimal target region 5208 identified as being areliable surface for the generation of the mating surfaces ofarthroplasty jigs may be limited to a lateral target area 5208A, theremainder of the lateral optimal target region 5208 being subjected tothe overestimation process. The lateral target area 5208A may be locatednear a central portion of the optimal target region 5208.

As indicated in FIG. 49A by the cross-hatching, in one embodiment, theanterior optimal target region 5204 may be an anterior surface of thetibia plateau region 5202 distal of the joint line or, morespecifically, distal of the anterior tibia plateau edge 5212. Theanterior optimal target region 5204 may be the anterior region of theproximal end of the tibia extending between the plateau edge 5212 andthe proximal edge 5255 of the tibia tuberosity 5214. The anterior targetregion 5204 may extend distally along the tibia adjacent to the medialand lateral edges 5256, 5257 of the tibia tuberosity 5214. The anteriortarget region 5204 may extend medially to the anterior medial edge 5260of the tibia, and laterally to the anterior lateral edge 5261 of thetibia.

As shown in FIG. 49E by the cross-hatching, the anterior optimal targetregion 5204 may be divided into three sub-regions 5204-1, 5204-2 and5204-3. The first or medial sub-region 5204-1 may be a generally planarsurface region that extends distally from generally the plateau edge5212 or capsule line 52E3 to a point generally even with the beginningof the distal half to distal third of the tibial tuberosity 5214. Themedial sub-region 5204-1 may extend medial-lateral from the medial edgeof the medial tibia condyle to a point generally even with a medial edgeof the tibial tuberosity 5214. The medial sub-region 5204-1 maygenerally taper is the distal direction to be generally triangular.

The second or middle sub-region 5204-2 may be a generally planar surfaceregion that extends distally from generally the plateau edge 5212 orcapsule line 52E3 to a point near the proximal boundary of the tibialtuberosity 5214. The middle sub-region 5204-2 may extend medial-lateralfrom the lateral edge of the medial sub-region 5204-1 to a pointgenerally even with a lateral edge of the tibial tuberosity 5214. Thefirst sub-region 5204-1 may be generally rectangular, with the longlength extending medial-lateral.

The third or lateral sub-region 5204-3 may be a generally planar surfaceregion that extends distally from generally the plateau edge 5212 orcapsule line 52E3 to a point generally even with the beginning of thedistal two-thirds to distal three-quarters of the tibial tuberosity5214. The lateral sub-region 5204-3 may extend medial-lateral from thelateral edge of the middle sub-region 5204-2 to a lateral edge of thelateral tibia condyle. The lateral sub-region 5204-3 may generally taperis the distal direction to be generally triangular.

In one embodiment as can be understood from FIGS. 49A and 49E, theanterior target region 5204 may be the entire cross-hatched region 5204or any one or more sub-regions 5204-1, 5204-2, 5204-3 of thecross-hatched region 5204 or any one or more portions of the sub-regions5204-1, 5204-2, 5204-3. For example, as indicated by the doublecross-hatching, each sub-region 5204-1, 5204-2 and 5204-3 may have arespective target area 5204-1A, 5204-2A and 5204-3A therein that may beidentified via the overestimation process disclosed herein. Thus,although the anterior optimal target region 5204, or more specifically,its sub-regions 5204-1, 5204-2, 5204-3 may be generally coextensive withthe three generally planar surface areas identified above with respectto FIG. 49E, the actual areas within the anterior optimal target region5204 identified as being a reliable surface for the generation of themating surfaces of arthroplasty jigs may be limited to an target areas5204-1A, 5204-2A and 5204-3A, the remainder of the sub-regions 5204-1,5204-2, 5204-3 being subjected to the overestimation process. Theanterior target areas 5204-1A, 5204-2A and 5204-3A may be located anywhere within the respective sub-regions 5204-1, 5204-2, 5204-3.

FIGS. 49B-C and are, respectively, top and bottom perspective views ofan example customized arthroplasty tibial jig 2B that has been generatedvia the overestimation process disclosed herein. Similar to the femoraljig 2A depicted in FIGS. 1H and 1I, the tibia jig 2B of FIGS. 49B-Cincludes an interior or bone-facing side 104 and an exterior side 106.When the jig 2B is mounted on the arthroplasty target region during asurgical procedure, the bone-facing side 104 faces the surface of thearthroplasty target region while the exterior side 106 faces in theopposite direction.

The interior or bone-facing side 104 of the tibia cutting jig 2Bincludes bone mating surfaces 40-5204, 40-5206 and 40-5208 that: aremachined into the jig interior or bone-facing side 104 based on contourlines that met the criterion of blocks 2508 and 2514 of FIG. 45E; andrespectively correspond to the optimal target regions 5204, 5206 and5208 of FIG. 49A. The rest 104′ of the interior or bone-facing side 104(i.e., the regions 104′ of the interior or bone facing sides 104 outsidethe bounds of bone mating surfaces 40-5204, 40-5206 and 40-5208) are theresult of the overestimation process wherein the corresponding contourlines failed to meet one or more of the criterion of blocks 2508 and2514 of FIG. 45E and, consequently, were moved away from the bonesurface. As a result, the interior side surface 104′ is machined to bespaced away from the bone surfaces of the arthroplasty target region soas to not contact the bone surfaces when the bone mating surfaces40-5204, 40-5206 and 40-5208 matingly receive and contact the bonesurfaces of the arthroplasty target region corresponding to regions5204, 5206 and 5208.

As can be understood from FIG. 49C, the medial bone mating surface40-5206 may include a smaller sub region bone mating surface 40-5206A,with the area of the medial bone mating surface 40-5206 outside thesmaller sub region mating surface 40-5206A being the result of theoverestimation process so as to not contact the corresponding bonesurface when the smaller sub region mating surface 40-5206A matinglyreceives and contacts its corresponding bone surface. The smaller subregion bone mating surface 40-5206A may be configured and positioned inthe jig inner surface 100 to matingly receive and contact the optimaltarget area 5206A discussed above with respect to FIGS. 49A and 49E.

As can be understood from FIG. 49C, the lateral bone mating surface40-5208 may include a smaller sub region bone mating surface 40-5208A,with the area of the lateral bone mating surface 40-5208 outside thesmaller sub region mating surface 40-5208A being the result of theoverestimation process so as to not contact the corresponding bonesurface when the smaller sub region mating surface 40-5208A matinglyreceives and contacts its corresponding bone surface. The smaller subregion bone mating surface 40-5208A may be configured and positioned inthe jig inner surface 100 to matingly receive and contact the optimaltarget area 5208A discussed above with respect to FIGS. 49A and 49E.

As can be understood from FIG. 49C, depending on the patient's bonetopography, the overestimation process disclosed herein may result in ananterior bone mating surface 40-5204 that is actually multiple bonemating surfaces have sub region mating surfaces that may besubstantially smaller than surface 5204 depicted in FIGS. 49A and 49E.For example, the anterior bone mating surface 40-5204 may actually bemade of an anterior medial bone mating surface 40-5204-1, an anteriormiddle bone mating surface 40-5204-2 and an anterior lateral bone matingsurface 40-5204-3. These mating surfaces 40-5204-1, 40-5204-2, 40-5204-3may have respective sub region bone mating surfaces 40-5204-1A,40-5204-2A, 40-5204-3A, with the areas of the mating surfaces 40-5204-1,40-5204-2, 40-5204-3 outside the respective sub region bone matingsurfaces 40-5204-1A, 40-5204-2A, 40-5204-3A being the result of theoverestimation process so as to not contact the corresponding bonesurfaces when the respective sub region bone mating surfaces 40-5204-1A,40-5204-2A, 40-5204-3A matingly receive and contact their respectivecorresponding bone surfaces. The sub region bone mating surfaces40-5204-1A, 40-5204-2A, 40-5204-3A may be configured and positioned inthe jig inner surface 100 to matingly receive and contact the respectiveoptimal target areas 5204-1A, 5204-2A, 5204-3A discussed above withrespect to FIGS. 49A and 49E.

As can be understood from FIG. 49D, which is a anterior-posteriorcross-section of the tibia jig 2B of FIGS. 49B-C mounted on the tibialproximal end 5200 of FIG. 49A, the interior or bone-facing side 104 isformed of bone mating surfaces 40-5204, 40-5206 and 40-5208 andspaced-apart surfaces 104′ (i.e., bone-facing surfaces 104 that are aproduct of the overestimation process and are spaced-apart from thecorresponding bone surfaces of the arthroplasty target region 5202). Asindicated by the plurality of opposed arrows in regions 5284, 5286 and5288, the bone mating surfaces 40-5204, 40-5206 and 40-5208 matinglyreceive and contact the corresponding bone surfaces 5204, 5206 and 5208to form mating surface contact regions 5284, 5286 and 5288. Conversely,the spaced-apart surfaces 104′ are spaced apart from the correspondingbone surfaces to form spaced-apart non-contact regions 5299, wherein thespaced-apart surfaces 104′ do not contact their corresponding bonesurfaces. In addition to having the mating surfaces 40-5204, 40-5206 and40-5208 and the spaced-apart surfaces 104′, the tibia jigs 2B may alsohave a saw cutting guide slot 30 and anterior and posterior drill holes45N and 32P, as discussed above.

The arrows in FIG. 49D represent a situation where the patient's bonetopography and the resulting overestimation process has generated bonemating surfaces 40-5204, 40-5206 and 40-5208 that match the targetregions 5204, 5206 and 5208, which are generally coextensive with theentirety of their respective potential regions as discussed above. Ofcourse, where the patient's bone topography and the resultingoverestimation process generates bone mating surfaces 40-5204-1A,40-5204-2A, 40-5204-3A, 40-5206A and 40-5208A that match the targetareas 5204-1A, 5204-2A, 5204-3A, 5206A and 5208A, which may besubstantially smaller than their respective target regions 5204-1,5204-2, 5204-3, 5206 and 5208, the mating surface contact regions 5284,5286 and 5288 may be smaller and/or segmented as compared to what isdepicted in FIG. 49D.

FIG. 49F depicts closed-loop contour lines 5302, 5304, and 5306 that areimage segmented from image slices, wherein the contour lines outline thecortical bone surface of the upper end of the tibia. These contour lines5302, 5304, and 5306 may be identified via image segmentation techniquesfrom medical imaging slices generated via, e.g., MRI or CT.

As shown in FIG. 49F, there are posterior portions of the contour lines(indicated as 5307) that may be of no interest during overestimationbecause the contour line region 5307 corresponds to a region of the kneethat may be inaccessible during surgery and may not correspond to a jigsurface because no part of the jig may access the region 5307 duringsurgery. There are also portions of the contour lines (indicated as5309) which may correspond generally to the plateau edge 5212 and maynot correspond to a jig surface because no part of the jig may abutagainst or matingly engage this contour line region 5309. An osteophytein contour line region 5308 may be identified based on the algorithm2500. The contour lines in region 5308 may be subsequently overestimated(based on the algorithm 2500) such that the resulting jig surface doesnot come into contact with the osteophyte (i.e., with the osteophytebone surface represented by contour line region 5308) when the jig'sbone mating surface 40 matingly receives and contacts the bone surfacesof the arthroplasty target region. Additionally, optimal contour lineregions 5310 and 5312 may be identified during execution of thealgorithm 2500 as areas of the patient's bone anatomy that have surfacevariations within the angular criteria of the algorithm 2500 and,therefore, are used to generate the jig's bone mating surface 40 thatmatingly receives and contacts the bone surfaces of the arthroplastytarget region.

Contour line region 5310 may pertain to region 5204 of FIG. 49A andtibia jig region 40-5204 of FIG. 49B. Contour line region 5312 maypertain to either region 5206 or 5208 of FIG. 49A and either tibia jigregion 40-5206 or 40-5208 of FIG. 49C.

Utilizing the optimal areas 4310 and 4312 as jig bone mating surfaces 40allows irregular areas of the patient's bone anatomy to be accommodatedwithout affecting the fit of the jig 2 to the patient's bone anatomy. Infact, an accurate and custom fit between the jig 2 and the patient'sbone anatomy can be made by using only a few of such optimal areas. Thisallows substantial overestimation of the jig surface in regionscorresponding to irregularities, thereby preventing the irregularitiesfrom interfering with an accurate and reliable fit between the jig'sbone mating surfaces and those bone surfaces of the arthroplasty targetregion corresponding to those bone mating surfaces. The result of theoverestimation process is a jig with bone mating surfaces that offer areliable and accurate custom fit with the arthroplasty target region.This may result in an increased success rate for TKR or partial kneereplacement surgery because the jig may custom fit to the most reliablebone surfaces and be deliberately spaced from the bone surfaces that maybe unreliable, for example, because of imaging or tool machinerylimitations.

As can be understood from FIGS. 49G and 49H, which are respectivelyanterior isometric views of the femur 3900 and tibia 5200, a patient'sbones 3900, 5200 may have regions that are more likely to be accuratelycomputer modeled from two dimensional medical image slices than otherregions of the patient's bones. Examples of such regions 3904, 3906,3908, 5204-1, 5204-2, 5204-3, 5206, and 5208 and how to determine suchregions are provided in the preceding discussion and also indicated inFIGS. 49G and 49H.

With respect to the articular regions 3906, 3908, 5206 and 5208 of thefemur 3900 and tibia 5200, in one embodiment, where the analysis ofblocks 2508 and 2514 of FIG. 45E indicate that there is little, if anycontour line variation along a specific contour line or between adjacentcontour lines, these regions 3906, 3908, 5206 and 5208 of the femur 3900and tibia 5200 may be understood to most closely approximatecircumferential surfaces 5400, 5500 of cylinders 5402, 5504 each havingan axis 5406, 5408, 5506, 5508 extending medial-lateral and having theirrespective circumferential surfaces 5400, 5500 superimposed onto thearticular regions 3906, 3908, 5206, 5208. Accordingly, such regions3906, 3908, 5206, 5208 may be likely to be readily accurately computermodeled.

In one embodiment, the circumferential surfaces 5400, 5500 may becorrespond to an elliptical cylinder having an elliptical cross sectiontransverse to its axis 5406, 5408, 5506, 5508 and having its ellipticalmajor axis extending generally anterior-posterior and is ellipticalminor axis extending generally proximal-distal. In one embodiment, thecircumferential surfaces 5400, 5500 may be correspond to an circularcylinder having an circular cross section transverse to its axis 5406,5408, 5506, 5508.

It should be noted that the overestimation process discussed above withrespect to FIGS. 45A-49H is useful for the generation of customizedarthroplasty jigs, regardless of whether the arthroplasty jigs areconfigured to produce natural alignment or zero degree or mechanicalaxis alignment for the patient's knee undergoing the arthroplastyprocedure. Also, the overestimation process discussed above may beemployed for both the generation of jigs for total knee arthroplasty andpartial or uni-compartmental knee arthroplasty. Furthermore, while theoverestimation process is discussed in the context of knee arthroplasty,those skilled in the art will readily recognize that the concepts taughtherein may be employed for the production of jigs for other types ofjoint arthroplasty, including, for example, arthroplasty for hip, ankle,elbow, shoulder, wrist, toe joint, finger joint, vertebra-vertebrainterfaces, vertebra-pelvis interfaces, vertebra-skull interfaces, etc.Accordingly, the overestimation processes and resulting jigs disclosedherein should be considered as being for all types of arthroplastyprocedures.

IV. Overview of Pre-operative Surgical Planning Process

Section II. of the present disclosure describes the acquisition ofmedical images, the segmentation or auto-segmentation of the medicalimages, and the generation of a patient bone model from the segmentedimages that is representative of the bones of the patient in adeteriorated or degenerated state. Section III. of the presentdisclosure describes an overestimation process where certain areas ofthe bone in the medical images are identified for generating mating jigsurfaces, and certain areas of the bone in the medical images areidentified as non-mating areas between a jig and the bone surface.Beginning in Section IV., the present disclosure describes exemplarymethods of implant planning (e.g., determining coordinate locations forresections, implant sizes) utilizing the bone models or image data(e.g., 2D image slices, restored 2D image slices) described previously.As described herein, the implant planning may take place utilizing theimage data (e.g., 2D image slices) of the bone models representative ofthe patient's bones in a pre-deteriorated state (described in SectionIII) or a deteriorated state (described in Section II).

Disclosed herein are customized arthroplasty jigs 2 and systems 4 for,and methods of, producing such jigs 2. The jigs 2 are customized to fitspecific bone surfaces of specific patients. Depending on theembodiment, the jigs 2 are automatically planned and generated and maybe similar to those disclosed in these three U.S. patent applications:U.S. patent application Ser. No. 11/656,323 to Park et al., titled“Arthroplasty Devices and Related Methods” and filed Jan. 19, 2007, nowU.S. Pat. No. 9,017,336; U.S. patent application Ser. No. 10/146,862 toPark et al., titled “Improved Total Joint Arthroplasty System” and filedMay 15, 2002; and U.S. patent Ser. No. 11/642,385 to Park et al., titled“Arthroplasty Devices and Related Methods” and filed Dec. 19, 2006. Thedisclosures of these three U.S. patent applications are incorporated byreference in their entireties into this Detailed Description.

A. Overview of System and Method for Manufacturing CustomizedArthroplasty Cutting Jigs

For an overview discussion of the systems 4 for, and methods of,producing the customized arthroplasty jigs 2, reference is made to FIGS.1A-1 l AND 50A-50E. FIG. 1A is a schematic diagram of a system 4 foremploying the automated jig production method disclosed herein. FIGS.50A-50E are flow chart diagrams outlining the jig production methoddisclosed herein. The following overview discussion can be broken downinto three sections.

The first section, which is discussed with respect to FIG. 1A and[blocks 100-125] of FIGS. 50A, 50B, 50C, and 50E, pertains to an examplemethod of determining, in a two-dimensional (“2D”) computer modelenvironment, saw cut and drill hole locations 30, 32 relative to 2Dimages 16 of a patient's joint 14. The resulting “saw cut and drill holedata” 44 is planned to provide saw cuts 30 and drill holes 32 that willallow arthroplasty implants to restore the patient's joint to itspre-degenerated or natural alignment state.

The second section, which is discussed with respect to FIG. 1A and[blocks 100-105 and 130-145] of FIGS. 50A, 50D, and 50E, pertains to anexample method of importing into 3D computer generated jig models 38 3Dcomputer generated surface models 40 of arthroplasty target areas 42 of3D computer generated arthritic models 36 of the patient's joint bones.The resulting “jig data” 46 is used to produce a jig customized tomatingly receive the arthroplasty target areas of the respective bonesof the patient's joint.

The third section, which is discussed with respect to FIG. 1A and[blocks 150-165] of FIG. 50E, pertains to a method of combining orintegrating the “saw cut and drill hole data” 44 with the “jig data” 46to result in “integrated jig data” 48. The “integrated jig data” 48 isprovided to the CNC machine 10 or other rapid production machine (e.g.,a stereolithography apparatus (“SLA”) machine) for the production ofcustomized arthroplasty jigs 2 from jig blanks 50 provided to the CNCmachine 10. The resulting customized arthroplasty jigs 2 include saw cutslots and drill holes positioned in the jigs 2 such that when the jigs 2matingly receive the arthroplasty target areas of the patient's bones,the cut slots and drill holes facilitate preparing the arthroplastytarget areas in a manner that allows the arthroplasty joint implants togenerally restore the patient's joint line to its pre-degenerated stateor natural alignment state.

As shown in FIG. 1A, the system 4 includes a computer 6 having a CPU 7,a monitor or screen 9 and an operator interface controls 11. Thecomputer 6 is linked to a medical imaging system 8, such as a CT or MRImachine 8, and a computer controlled machining system 10, such as a CNCmilling machine 10.

As indicated in FIG. 1A, a patient 12 has a joint 14 (e.g., a knee,elbow, ankle, wrist, hip, shoulder, skull/vertebrae orvertebrae/vertebrae interface, etc.) to be replaced. The patient 12 hasthe joint 14 scanned in the imaging machine 8. The imaging machine 8makes a plurality of scans of the joint 14, wherein each scan pertainsto a thin slice of the joint 14.

As can be understood from FIG. 50A, the plurality of scans is used togenerate a plurality of two-dimensional (“2D”) images 16 of the joint 14[block 100 z]. Where, for example, the joint 14 is a knee 14, the 2Dimages will be of the femur 18 and tibia 20. The imaging may beperformed via CT or MRI. In one embodiment employing MRI, the imagingprocess may be as disclosed in U.S. patent application Ser. No.11/946,002 to Park, which is entitled “Generating MRI Images Usable ForThe Creation Of 3D Bone Models Employed To Make Customized ArthroplastyJigs,” was filed Nov. 27, 2007 and is incorporated by reference in itsentirety into this Detailed Description. The images 16 may be a varietyof orientations, including, for example, sagittal 2D images, coronal 2Dimages and axial 2D images.

As can be understood from FIG. 1A, the 2D images are sent to thecomputer 6 for analysis and for creating computer generated 2D modelsand 3D models. In one embodiment, the bone surface contour lines of thebones 18, 20 depicted in the image slices 16 may be auto segmented viaan image segmentation process as disclosed in U.S. Patent Application61/126,102, which was filed Apr. 30, 2008, is entitled System and Methodfor Image Segmentation in Generating Computer Models of a Joint toUndergo Arthroplasty, and is hereby incorporated by reference into thepresent application in its entirety.

As indicated in FIG. 50A, in one embodiment, reference point W isidentified in the 2D images 16 [block 105]. In one embodiment, asindicated in [block 105] of FIG. 1A, reference point W may be at theapproximate medial-lateral and anterior-posterior center of thepatient's joint 14. In other embodiments, reference point W may be atany other location in the 2D images 16, including anywhere on, near oraway from the bones 18, 20 or the joint 14 formed by the bones 18, 20.Reference point W may be defined at coordinates (X0-j, Y0-j, Z0-j)relative to an origin (X0, Y0, Z0) of an X-Y-Z axis and depicted inFIGS. 50A-50D as W (X0-j, Y0-j, Z0-j). Throughout the processesdescribed herein, to allow for correlation between the different typesof images, models or any other data created from the images, movementsof such images, models or any other data created form the images may betracked and correlated relative to the origin.

As described later in this overview, point W may be used to locate the2D images 16 and computer generated 3D model 36 created from the 2Dimages 16 respectively with the implant images 34 and jig blank model 38and to integrate information generated via the POP process. Depending onthe embodiment, point W, which serves as a position and/or orientationreference, may be a single point, two points, three points, a point plusa plane, a vector, etc., so long as the reference point W can be used toposition and/or orient the 2D images 16, 34 and 3D models 36, 38relative to each other as needed during the POP process.

As shown in FIG. 50B, the coronal and axial 2D images 16 of the femur 18forming the patient's joint 14 are analyzed to determine femur referencedata [block 110]. For example, the coronal 2D images are analyzed todetermine the most distal femur point D1 on a healthy condyle and ajoint line perpendicular to a trochlear groove line is used to estimatethe location of a hypothetical most distal point D2 on the damagedcondyle. Similarly, the axial 2D images are analyzed to determine themost posterior femur point P1 on a healthy condyle and a joint lineperpendicular to a trochlear groove line is used to estimate thelocation of a hypothetical most posterior point P2 on the damagedcondyle. The femur reference data points D1, D2, P1, P2 is mapped orotherwise imported to a sagittal or y-z plane in a computer environmentand used to determine the sagittal or y-z plane relationship between thefemur reference data points D1, D2, P1, P2. The femur reference data D1,D2, P1, P2 is then used to choose candidate femoral implant(s). [Block112]. The femur reference data points D1, D2, P1, P2 are respectivelycorrelated with similar reference data points D1′, D2′, P1′, P2′ of theselected femur implant 34 in a sagittal or y-z plane [block 114]. Thiscorrelation determines the locations and orientations of the cut plane30 and drill holes 32 needed to cause the patient's joint to returned toa natural, pre-deteriorated alignment with the selected implant 34. Thecut plane 30 and drill hole 32 locations determined in block 114 areadjusted to account for cartilage thickness [block 118].

As shown in FIG. 50C at block 120, tibia reference data is determinedfrom the images in a manner similar to the process of block 110, exceptdifferent image planes are employed. Specifically, sagittal and coronalimages slices of the tibia are analyzed to identify the lowest (i.e.,most distal) and most anterior and posterior points of the tibiarecessed condylar surfaces. This tibia reference data is then projectedonto an axial view. The tibia reference data is used to select anappropriate tibia implant [Block 121]. The tibia reference data iscorrelated to similar reference data of the selected tibia implant in amanner similar to that of block 114, except the correlation takes placein an axial view [Block 122]. The cut plane 30 associated with the tibiaimplant's position determined according to block 122 is adjusted toaccount for cartilage thickness [Block 123].

Once the saw cut locations 30 and drill hole locations 32 associatedwith the POP of the femur and tibia implants 34 has been completed withrespect to the femur and tibia data 28 (e.g., the 2D femur and tibiaimages 16 and reference point W), the saw cut locations 30 and drillhole locations 32 are packaged relative to the reference point W(X0-j,Y0-j, Z0-j) [Block 124]. As the images 16 and other data created fromthe images or by employing the images may have moved during any of theprocesses discussed in blocks 110-123, the reference point W(X0-j, Y0-j,Z0-j) for the images or associated data may become updated referencepoint W′ at coordinates (X0-k, Y0-k, Z0-k) relative to an origin (X0,Y0, Z0) of an X-Y-Z axis. For example, during the correlation processdiscussed in blocks 114 and 122, the implant reference data may be movedtowards the bone image reference data or, alternatively, the bone imagereference data may be moved towards the implant reference data. In thelatter case, the location of the bone reference data will move fromreference point W(X0-j, Y0-j, Z0-j) to updated reference point W′(X0-k,Y0-k, Z0-k), and this change in location with respect to the origin willneed to be matched by the arthritic models 36 to allow for “saw cut anddrill hole” data 44 obtained via the POP process of blocks 110-125 to bemerged with “jig data” 46 obtained via the jig mating surface definingprocess of blocks 130-145, as discussed below.

As can be understood from FIG. 50E, the POP process may be completedwith the packaging of the saw cut locations 30 and drill hole locations32 with respect to the updated reference point W′(X0-k, Y0-k, Z0-k) as“saw cut and drill hole data” 44 [Block 125]. The “saw cut and drillhole data” 44 is then used as discussed below with respect to [block150] in FIG. 50E.

In one embodiment, the POP procedure is a manual process, wherein 2Dbone images 28 (e.g., femur and tibia 2D images in the context of thejoint being a knee) are manually analyzed to determine reference data toaid in the selection of a respective implant 34 and to determine theproper placement and orientation of saw cuts and drill holes that willallow the selected implant to restore the patient's joint to itsnatural, pre-deteriorated state. (The reference data for the 2D boneimages 28 may be manually calculated or calculated by a computer by aperson sitting in front of a computer 6 and visually observing theimages 28 on the computer screen 9 and determining the reference datavia the computer controls 11. The data may then be stored and utilizedto determine the candidate implants and proper location and orientationof the saw cuts and drill holes. In other embodiments, the POP procedureis totally computer automated or a combination of computer automationand manual operation via a person sitting in front of the computer.

In some embodiments, once the selection and placement of the implant hasbeen achieved via the 2D POP processes described in blocks 110-125, theimplant selection and placement may be verified in 2D by superimposingthe implant models 34 over the bone images data, or vice versa.Alternatively, once the selection and placement of the implant has beenachieved via the 2D POP processes described in blocks 110-125, theimplant selection and placement may be verified in 3D by superimposingthe implant models 34 over 3D bone models generated from the images 16.Such bone models may be representative of how the respective bones mayhave appeared prior to degeneration. In superimposing the implants andbones, the joint surfaces of the implant models can be aligned or causedto correspond with the joint surfaces of the 3D bone models. This endsthe overview of the POP process. A more detailed discussion of variousembodiments of the POP process is provided later in this DetailedDescription

As can be understood from FIG. 50D, the 2D images 16 employed in the 2DPOP analysis of blocks 110-124 of FIGS. 50B-50C are also used to createcomputer generated 3D bone and cartilage models (i.e., “arthriticmodels”) 36 of the bones 18, 20 forming the patient's joint 14 [block130]. Like the above-discussed 2D images and femur and tibia referencedata, the arthritic models 36 are located such that point W is atcoordinates (X0-j, Y0-j, Z0-j) relative to the origin (X0, Y0, Z0) ofthe X-Y-Z axis [block 130]. Thus, the 2D images and femur and tibia dataof blocks 110-125 and arthritic models 36 share the same location andorientation relative to the origin (X0, Y0, Z0). Thisposition/orientation relationship is generally maintained throughout theprocess discussed with respect to FIGS. 50A-50E. Accordingly, movementsrelative to the origin (X0, Y0, Z0) of the 2D images and femur and tibiadata of blocks 110-125 and the various descendants thereof (i.e., bonecut locations 30 and drill hole locations 32) are also applied to thearthritic models 36 and the various descendants thereof (i.e., the jigmodels 38). Maintaining the position/orientation relationship betweenthe 2D images and femur and tibia data of blocks 110-125 and arthriticmodels 36 and their respective descendants allows the “saw cut and drillhole data” 44 to be integrated into the “jig data” 46 to form the“integrated jig data” 48 employed by the CNC machine 10 to manufacturethe customized arthroplasty jigs 2, as discussed with respect to block150 of FIG. 50E.

Computer programs for creating the 3D computer generated arthriticmodels 36 from the 2D images 16 include: Analyze from AnalyzeDirect,Inc., Overland Park, Kans.; Insight Toolkit, an open-source softwareavailable from the National Library of Medicine Insight Segmentation andRegistration Toolkit (“ITK”), www.itk.org; 3D Slicer, an open-sourcesoftware available from www.slicer.org; Mimics from Materialise, AnnArbor, Mich.; and Paraview available at www.paraview.org.

The arthritic models 36 depict the bones 18, 20 in the presentdeteriorated condition with their respective degenerated joint surfaces24, 26, which may be a result of osteoarthritis, injury, a combinationthereof, etc. The arthritic models 36 also include cartilage in additionto bone. Accordingly, the arthritic models 36 depict the arthroplastytarget areas 42 generally as they will exist when the customizedarthroplasty jigs 2 matingly receive the arthroplasty target areas 42during the arthroplasty surgical procedure.

As indicated in FIG. 50D and already mentioned above, to coordinate thepositions/orientations of the 2D images and femur and tibia data ofblocks 110-125 and arthritic models 36 and their respective descendants,any movement of the 2D images and femur and tibia data of blocks 110-125from point W to point W′ is tracked to cause a generally identicaldisplacement for the “arthritic models” 36, and vice versa [block 135].

As depicted in FIG. 50D, computer generated 3D surface models 40 of thearthroplasty target areas 42 of the arthritic models 36 are importedinto computer generated 3D arthroplasty jig models 38 [block 140]. Thus,the jig models 38 are configured or indexed to matingly receive thearthroplasty target areas 42 of the arthritic models 36. Jigs 2manufactured to match such jig models 38 will then matingly receive thearthroplasty target areas of the actual joint bones during thearthroplasty surgical procedure.

In one embodiment, the procedure for indexing the jig models 38 to thearthroplasty target areas 42 is a manual process. The 3D computergenerated models 36, 38 are manually manipulated relative to each otherby a person sitting in front of a computer 6 and visually observing thejig models 38 and arthritic models 36 on the computer screen 9 andmanipulating the models 36, 38 by interacting with the computer controls11. In one embodiment, by superimposing the jig models 38 (e.g., femurand tibia arthroplasty jigs in the context of the joint being a knee)over the arthroplasty target areas 42 of the arthritic models 36, orvice versa, the surface models 40 of the arthroplasty target areas 42can be imported into the jig models 38, resulting in jig models 38indexed to matingly receive the arthroplasty target areas 42 of thearthritic models 36. Point W′ (X0-k, Y0-k, Z0-k) can also be importedinto the jig models 38, resulting in jig models 38 positioned andoriented relative to point W′ (X0-k, Y0-k, Z0-k) to allow theirintegration with the bone cut and drill hole data 44 of [block 125].

In one embodiment, the procedure for indexing the jig models 38 to thearthroplasty target areas 42 is generally or completely automated, asdisclosed in U.S. patent application Ser. No. 11/959,344 to Park, whichis entitled System and Method for Manufacturing Arthroplasty Jigs, wasfiled Dec. 18, 2007, now U.S. Pat. No. 8,221,430 and is incorporated byreference in its entirety into this Detailed Description. For example, acomputer program may create 3D computer generated surface models 40 ofthe arthroplasty target areas 42 of the arthritic models 36. Thecomputer program may then import the surface models 40 and point W′(X0-k, Y0-k, Z0-k) into the jig models 38, resulting in the jig models38 being indexed to matingly receive the arthroplasty target areas 42 ofthe arthritic models 36. The resulting jig models 38 are also positionedand oriented relative to point W′ (X0-k, Y0-k, Z0-k) to allow theirintegration with the bone cut and drill hole data 44 of [block 125].

In one embodiment, the arthritic models 36 may be 3D volumetric modelsas generated from the closed-loop process discussed in U.S. patentapplication Ser. No. 11/959,344 filed by Park. In other embodiments, thearthritic models 36 may be 3D surface models as generated from theopen-loop process discussed in U.S. patent application Ser. No.11/959,344 filed by Park.

In one embodiment, the models 40 of the arthroplasty target areas 42 ofthe arthritic models 36 may be generated via an overestimation processas disclosed in U.S. Provisional Patent Application 61/083,053, which isentitled System and Method for Manufacturing Arthroplasty Jigs HavingImproved Mating Accuracy, was filed by Park Jul. 23, 2008, and is herebyincorporated by reference in its entirety into this DetailedDescription.

As indicated in FIG. 50E, in one embodiment, the data regarding the jigmodels 38 and surface models 40 relative to point W′ (X0-k, Y0-k, Z0-k)is packaged or consolidated as the “jig data” 46 [block 145]. The “jigdata” 46 is then used as discussed below with respect to [block 150] inFIG. 50E.

As can be understood from FIG. 50E, the “saw cut and drill hole data” 44is integrated with the “jig data” 46 to result in the “integrated jigdata” 48 [block 150]. As explained above, since the “saw cut and drillhole data” 44, “jig data” 46 and their various ancestors (e.g., 2Dimages and femur and tibia data of blocks 110-125 and models 36, 38) arematched to each other for position and orientation relative to point Wand W′, the “saw cut and drill hole data” 44 is properly positioned andoriented relative to the “jig data” 46 for proper integration into the“jig data” 46. The resulting “integrated jig data” 48, when provided tothe CNC machine 10, results in jigs 2: (1) configured to matinglyreceive the arthroplasty target areas of the patient's bones; and (2)having cut slots and drill holes that facilitate preparing thearthroplasty target areas in a manner that allows the arthroplasty jointimplants to generally restore the patient's joint line to itspre-degenerated state or natural alignment state.

As can be understood from FIGS. 1A and 50E, the “integrated jig data” 44is transferred from the computer 6 to the CNC machine 10 [block 155].Jig blanks 50 are provided to the CNC machine 10 [block 160], and theCNC machine 10 employs the “integrated jig data” to machine thearthroplasty jigs 2 from the jig blanks 50 [block 165].

For a discussion of example customized arthroplasty cutting jigs 2capable of being manufactured via the above-discussed process, referenceis made to FIGS. 51A-51D. While, as pointed out above, theabove-discussed process may be employed to manufacture jigs 2 configuredfor arthroplasty procedures involving knees, elbows, ankles, wrists,hips, shoulders, vertebra interfaces, etc., the jig examples depicted inFIGS. 51A-51D are for total knee replacement (“TKR”) or partial knee(“uni-knee”) replacement procedures. Thus, FIGS. 51A and 51B are,respectively, bottom and top perspective views of an example customizedarthroplasty femur jig 2A, and FIGS. 51C and 51D are, respectively,bottom and top perspective views of an example customized arthroplastytibia jig 2B.

As indicated in FIGS. 51A and 51B, a femur arthroplasty jig 2A mayinclude an interior side or portion 98 and an exterior side or portion102. When the femur cutting jig 2A is used in a TKR procedure, theinterior side or portion 98 faces and matingly receives the arthroplastytarget area 42 of the femur lower end, and the exterior side or portion102 is on the opposite side of the femur cutting jig 2A from theinterior portion 98.

The interior portion 98 of the femur jig 2A is configured to match thesurface features of the damaged lower end (i.e., the arthroplasty targetarea 42) of the patient's femur 18. Thus, when the target area 42 isreceived in the interior portion 98 of the femur jig 2A during the TKRsurgery, the surfaces of the target area 42 and the interior portion 98match. The cutting jig 2A may include one or more saw guiding slots 123and one or more drill holes 124.

The surface of the interior portion 98 of the femur cutting jig 2A ismachined or otherwise formed into a selected femur jig blank 50A and isbased or defined off of a 3D surface model 40 of a target area 42 of thedamaged lower end or target area 42 of the patient's femur 18.

As indicated in FIGS. 51C and 51D, a tibia arthroplasty jig 2B mayinclude an interior side or portion 104 and an exterior side or portion106. When the tibia cutting jig 2B is used in a TKR procedure, theinterior side or portion 104 faces and matingly receives thearthroplasty target area 42 of the tibia upper end, and the exteriorside or portion 106 is on the opposite side of the tibia cutting jig 2Bfrom the interior portion 104.

The interior portion 104 of the tibia jig 2B is configured to match thesurface features of the damaged upper end (i.e., the arthroplasty targetarea 42) of the patient's tibia 20. Thus, when the target area 42 isreceived in the interior portion 104 of the tibia jig 2B during the TKRsurgery, the surfaces of the target area 42 and the interior portion 104match.

The surface of the interior portion 104 of the tibia cutting jig 2B ismachined or otherwise formed into a selected tibia jig blank 50B and isbased or defined off of a 3D surface model 40 of a target area 42 of thedamaged upper end or target area 42 of the patient's tibia 20. Thecutting jig 2B may include one or more saw guiding slots 123 and one ormore drill holes 124.

While the discussion provided herein is given in the context of TKR andTKR jigs and the generation thereof, the disclosure provided herein isreadily applicable to uni-compartmental or partial arthroplastyprocedures in the knee or other joint contexts. Thus, the disclosureprovided herein should be considered as encompassing jigs and thegeneration thereof for both total and uni-compartmental arthroplastyprocedures.

The remainder of this Detailed Discussion will now focus on variousembodiments for performing POP.

B. Overview of Preoperative Planning (“POP”) Procedure

In one embodiment, as can be understood from [blocks 100-110] of FIGS.50A-50C, medical images 16 of the femur and tibia 18, 20 are generated[blocks 100 and 105] and coronal, axial and sagittal image slices areanalyzed to determine reference data 28, 100 z, 900 z. [Block 115]. Thesizes of the implant models 34 are selected relative to the femur andtibia reference data. [Block 112, 114 and 121, 122]. The reference data28, 100 z, 900 z is utilized with the data associated with implantmodels 34 to determine the cut plane location. The joint spacing betweenthe femur and the tibia is determined. An adjustment value tr isdetermined to account for cartilage thickness or joint gap of a restoredjoint. The implant models 34 are shifted or adjusted according to theadjustment value tr [blocks 118 and 123]. Two dimensional computerimplant models 34 are rendered into the two dimensional imaging slice(s)of the bones 28 such that the 2D implant models 34 appear alongside the2D imaging slices of the bones 28. In one embodiment, ITK software,manufactured by Kitware, Inc. of Clifton Park, N.Y. is used to performthis rendering. Once the 2D implant models 34 are rendered into theMRI/CT image, the proper selection, orientation and position of theimplant models can be verified. An additional verification process maybe used wherein 3D models of the bones and implants are created andproper positioning of the implant may be verified. Two dimensionalcomputer models 34 and three dimensional computer models 1004 z, 1006 zof the femur and tibia implants are generated from engineering drawingsof the implants and may be generated via any of the above-referenced 2Dand 3D modeling programs to confirm planning. If the implant sizing isnot correct, then the planning will be amended by further analysis ofthe 2D images. If the implant sizing is accurate, then planning iscomplete. The process then continues as indicated in [block 125] of FIG.50E.

This ends the overview of the POP process. The following discussionswill address each of the aspects of the POP process in detail.

C. Femur and Tibia Images

FIG. 52A depicts 3D bone models or images 28′, 28″ of the femur andtibia 18, 20 from medical imaging scans 16. While FIG. 52A representsthe patient's femur 18 and tibia 20 prior to injury or degeneration(such as, for example, in the case of the femur and tibia restored bonemodels 28A, 28B of FIGS. 42D and 42E), it can be understood that, inother embodiments, the images 28′, 28″ may also represent the patient'sfemur 18 and tibia 20 after injury or degeneration (such as, forexample, the femur bone model 22A in FIG. 44A and the tibia bone model22B in FIG. 44B). More specifically, FIG. 52A is a 3D bone model 28′ ofa femur lower end 200 z and an 3D bone model 28″ of a tibia upper end205 z representative of the corresponding patient bones 18, 20 in anon-deteriorated state and in position relative to each to form a kneejoint 14. The femur lower end 200 z includes condyles 215 z, and thetibia upper end 205 z includes a plateau 220 z. The images or bonemodels 28′, 28″ are positioned relative to each other such that thecurved articular surfaces of the condyles 215 z, which would normallymate with complementary articular surfaces of the plateau 220 z, areinstead not mating, but roughly positioned relative to each other togenerally approximate the knee joint 14.

As generally discussed above with respect to FIGS. 50A-50C, the POPbegins by using a medical imaging process, such as magnetic resonanceimaging (MRI), computed tomography (CT), and/or another other medicalimaging process, to generate imaging data of the patient's knee. Forexample, current commercially available MRI machines use 8 bit (255grayscale) to show the human anatomy. Therefore, certain components ofthe knee, such as the cartilage, cortical bone, cancellous bone,meniscus, etc., can be uniquely viewed and recognized with 255grayscale. The generated imaging data is sent to a preoperative planningcomputer program. Upon receipt of the data, a user or the computerprogram may analyze the data (e.g., two-dimensional MRI images 16, andmore specifically, the 2D femur image(s) 28′ or 2D tibia image(s) 28″)to determine various reference points, reference lines and referenceplanes. In one embodiment, the MRI imaging scans 16 may be analyzed andthe reference data for POP may be generated by a proprietary softwareprogram called PerForm.

For greater detail regarding the methods and systems for computermodeling joint bones, such as the femur and tibia bones forming theknee, please see the following U.S. patent applications, which are allincorporated herein in their entireties: U.S. patent application Ser.No. 11/656,323 to Park et al., titled “Arthroplasty Devices and RelatedMethods” and filed Jan. 19, 2007, now U.S. Pat. No. 9,017,336; U.S.patent application Ser. No. 10/146,862 to Park et al., titled “ImprovedTotal Joint Arthroplasty System” and filed May 15, 2002; U.S. patentSer. No. 11/642,385 to Park et al., titled “Arthroplasty Devices andRelated Methods” and filed Dec. 19, 2006.

FIG. 52B is an isometric view of a computer model of a femur implant 34′and a computer model of a tibia implant 34″ in position relative to eachto form an artificial knee joint 14. The computer models 34′, 34″ may beformed, for example, via computer aided drafting or 3D modelingprograms. As will be discussed later in this detailed description, theimplant computer models may be in 2D or in 3D as necessary for theparticular planning step.

The femur implant model 34′ will have a joint side 240 z and a boneengaging side 245 z. The joint side 240 z will have a condyle-likesurface for engaging a complementary surface of the tibia implant model34″. The bone engaging side 245 z will have surfaces and engagementfeatures 250 z for engaging the prepared (i.e., sawed to shape) lowerend of the femur 18.

The tibia implant model 34″ will have a joint side 255 z and a boneengaging side 260 z. The joint side 255 z will have a plateau-likesurface configured to engage the condyle-like surface of the femurimplant model 34′. The bone engaging side 260 z will have an engagementfeature 265 z for engaging the prepared (i.e., sawed to shape) upper endof the tibia 20.

As discussed in the next subsections of this Detailed Description, thereference data of the femur and tibia bone models or images 28′, 28″ maybe used in conjunction with the implant models 34′, 34″ to select theappropriate sizing for the implants actually to be used for the patient.The resulting selections can then be used for planning purposes, asdescribed later in this Detailed Description.

D. Femur Planning Process

For a discussion of the femur planning process, reference is now made toFIGS. 53-58. FIGS. 53-58 illustrate a process in the POP wherein thesystem 4 utilizes 2D imaging slices (e.g., MRI slices, CT slices, etc.)to determine femur reference data, such as reference points, lines andplanes via their relationship to the trochlear groove plane-GHO of thefemur. The resulting femur reference data 100 z is then mapped orprojected to a y-z coordinate system (sagittal plane). The femurreference data is then applied to a candidate femur implant model,resulting in femoral implant reference data 100 z′. The data 100 z, 100z′ is utilized to select an appropriate set of candidate implants, fromwhich a single candidate implant will be chosen, which selection will bediscussed in more detail below with reference to FIGS. 59-71.

1. Determining Femur Reference Data

For a discussion of a process used to determine the femur referencedata, reference is now made to FIGS. 53-56C. FIG. 53 is a perspectiveview of the distal end of a 3D model 1000 z of the femur image of FIG.52A wherein the femur reference data 100 z is shown. As will beexplained in more detail below, the femur reference data is generated byan analysis of the 2D image scans and FIG. 53 depicts the relativepositioning of the reference data on a 3D model. As shown in FIG. 53,the femur reference data 100 z may include reference points (e.g. D1,D2), reference lines (e.g. GO, EF) and reference planes (e.g. P, S). Thefemur reference data 100 z may be determined by a process illustrated inFIGS. 54A-56D and described in the next sections.

As shown in FIG. 54A, which is a sagittal view of a femur 18illustrating the orders and orientations of imaging slices 16 that areutilized in the femur POP, a multitude of image slices may be compiled.In some embodiments, the image slices may be analyzed to determine, forexample, distal contact points prior to or instead of being compiledinto a bone model. Image slices may extend medial-lateral in planes thatwould be normal to the longitudinal axis of the femur, such as imageslices 1-5 of FIGS. 54A and 55D. Image slices may extend medial-lateralin planes that would be parallel to the longitudinal axis of the femur,such as image slices 6-9 of FIGS. 54A and 56B. The number of imageslices may vary from 1-50 and may be spaced apart in a 2 mm spacing orother spacing.

a. Determining Reference Points P1P2

In some embodiments, the planning process begins with the analysis ofthe femur slices in a 2D axial view. As can be understood from FIG. 54B,which depicts axial imaging slices of FIG. 54A, the series of 2D axialfemur slices are aligned to find the most posterior point of eachcondyle. For example, the most posterior points of slice 5, P1A, P2A,are compared to the most posterior points of slice 4, P1B, P2B. The mostposterior points of slice 4 are more posterior than those of slice 5.Therefore, the points of slice 4 will be compared to slice 3. The mostposterior points of slice 3, P1C, P2C, are more posterior than theposterior points P1B, P2B of slice 4. Therefore, the points of slice 3will be compared to slice 2. The most posterior points of slice 2, P1D,P2D, are more posterior than the posterior points P1C, P2C of slice 3.Therefore, the points of slice 2 will be compared to slice 1. The mostposterior points of slice 1, P1E, P2E, are more posterior than theposterior points P1D, P2D of slice 2. In some embodiments, the points ofslice 1 may be compared to slice 0 (not shown). The most posteriorpoints of slice 0, P1F, P2F, are less posterior than the posteriorpoints P1E, P2E of slice 1. Therefore, the points of slice 1 aredetermined to be the most posterior points P1P2 of the femur. In someembodiments, points P1 and P2 may be found on different axial slices.That is, the most posterior point on the medial side and most posteriorpoint on the lateral side may lie in different axial slices. Forexample, slice 2 may include the most posterior point on the lateralside, while slice 1 may include the most posterior point on the medialside. It can be appreciated that the number of slices that are analyzedas described above may be greater than five slices or less than fiveslices. The points P1, P2 are stored for later analysis.

b. Determining Reference Points D1, D2

The planning process continues with the analysis of the femur slices ina 2D coronal view. As can be understood from FIG. 54C, which depictscoronal imaging slices of FIG. 54A, the series of 2D coronal femurslices are aligned to find the most distal point of each condyle. Forexample, the most distal points of slice 6, D1A, D2A, are compared tothe most distal points of slice 7, D1B, D2B. The most distal points ofslice 7 are more distal than those of slice 6. Therefore, the points ofslice 7 will be compared to slice 8. The most distal points of slice 8,D1C, D2C, are more distal than the distal points D1B, D2B of slice 7.Therefore, the points of slice 8 will be compared to slice 9. The mostdistal points of slice 9, D1D, D2D, are more distal than the distalpoints D1C, D2C of slice 8. In some embodiments, the points of slice 9may be compared to slice 10 (not shown). The most distal points of slice10, D1E, D2E, are less distal than the distal points D1D, D2D of slice9. Therefore, the points of slice 9 are determined to be the most distalpoints D1, D2 of the femur. In some embodiments, points D1 and D2 may befound on different coronal slices. That is, the most distal point on themedial side and most distal point on the lateral side may lie indifferent coronal slices. For example, slice 9 may include the mostdistal point on the lateral side, while slice 8 may include the mostdistal point on the medial side. It can be appreciated that the numberof slices that are analyzed as described above may be greater than fourslices or less than four slices. The points D1, D2 are stored for futureanalysis.

c. Determining Reference Lines CD and GO

Analysis of the 2D slices in the axial view aid in the determination ofinternal/external rotation adjustment. The points D1, D2 represent thelowest contact points of each of the femoral lateral and medial condyles302 z, 303 z. Thus, to establish an axial-distal reference line, lineCD, in 2D image slice(s), the analysis utilizes the most distal point,either D1 or D2

, from the undamaged femoral condyle. For example, as shown in FIG. 55A,which is an axial imaging slice of the femur of FIG. 54A, when thelateral condyle 302 z is undamaged but the medial condyle 303 z isdamaged, the most distal point D1 will be chosen as the reference pointin establishing the axial-distal reference line, line CD. The line CD isextended from the lateral edge of the lateral condyle, through point D1,to the medial edge of the medial condyle. If the medial condyle wasundamaged, then the distal point D2 would be used as the reference pointthrough which line CD would be extended. The distal points D1, D2 andline CD are stored for later analysis.

A line CD is verified. A most distal slice of the series of axial viewsis chosen to verify the position of an axial-distal reference line, lineCD. As shown in FIG. 55A, the most distal slice 300 z of the femur(e.g., slice 5 in FIGS. 54A and 55D) is chosen to position line CD suchthat line CD is generally anteriorly-posteriorly centered in the lateraland medial condyles 302 z, 303 z. Line CD is generally aligned with thecortical bone of the undamaged posterior condyle. For example, if themedial condyle 303 z is damaged, the line CD will be aligned with theundamaged lateral condyle, and vice versa. To verify the location ofline CD and as can be understood from FIGS. 53 and 55C, the line CD willalso connect the most distal points D1, D2. The geography information ofline CD will be stored for future analysis.

Line GO is determined. The “trochlear groove axis” or the “trochleargroove reference plane” is found. In the knee flexion/extension motionmovement, the patella 304 z generally moves up and down in the femoraltrochlear groove along the vertical ridge and generates quadricepsforces on the tibia. The patellofemoral joint and the movement of thefemoral condyles play a major role in the primary structure andmechanics across the joint. In a normal knee model or properly alignedknee, the vertical ridge of the posterior patella is generally straight(vertical) in the sliding motion. For the OA patients' knees, there israrely bone damage in the trochlear groove; there is typically onlycartilage damage. Therefore, the trochlear groove of the distal femurcan serve as a reliable bone axis reference. In relation to the jointline assessment, as discussed with reference to FIGS. 63A-63J, thetrochlear groove axis of the distal femur is perpendicular or nearlyperpendicular to the joint line of the knee. A detailed discussion ofthe trochlear groove axis or the trochlear groove reference plane may befound in co-owned U.S. patent application Ser. No. 12/111,924, now U.S.Pat. No. 8,480,679, which is incorporated by reference in its entirety.

To perform the trochlear groove analysis, the MRI slice in the axialview with the most distinct femoral condyles (e.g., the slice with thelargest condyles such as slice 400 z of FIG. 55B) will be chosen toposition the trochlear groove bisector line, line TGB. As shown in FIG.55B, which is an axial imaging slice of the femur of FIG. 54A, the mostdistinct femoral condyles 302 z, 303 z are identified. The trochleargroove 405 z is identified from image slice 400 z. The lowest extremity406 z of the trochlear groove 405 z is then identified. Line TGB is thengenerally aligned with the trochlear groove 405 z across the lowestextremity 406 z. In addition, and as shown in FIG. 55D, which is theaxial imaging slices 1-5 taken along section lines 1-5 of the femur inFIG. 54A, each of the slices 1-5 can be aligned vertically along thetrochlear groove 405 z, wherein points G1, G2, G3, G4, G5 respectivelyrepresent the lowest extremity 406 z of trochlear groove 405 z for eachslice 1-5. By connecting the various points G1, G2, G3, G4, G5, a point0 can be obtained. As can be understood from FIGS. 53 and 55C, resultingline GO is perpendicular or nearly perpendicular to line D1 D2. In a 90°knee extension, line GO is perpendicular or nearly perpendicular to thejoint line of the knee and line P1P2. Line GO is stored for lateranalysis.

d. Determining Reference Lines EF and HO

Analysis of the 2D slices in the coronal view aid in the determinationof femoral varus/valgus adjustment. The points P1, P2 determined aboverepresent the most posterior contact points of each of the femorallateral and medial condyles 302 z, 303 z. Thus, to establish a coronalposterior reference line, line EF, in 2D image slice(s), the analysisutilizes the most posterior point, either P1 or P2, from the undamagedfemoral condyle. For example, as shown in FIG. 56A, when the lateralcondyle 302 z is undamaged but the medial condyle 303 z is damaged, themost posterior point P1 will be chosen as the reference point inestablishing the coronal posterior reference line, line EF. The line EFis extended from the lateral edge of the lateral condyle, through pointP1, to the medial edge of the medial condyle. If the medial condyle wasundamaged, then the posterior point P2 would be used as the referencepoint through which line EF would be extended. The posterior points P1,P2 and line EF are stored for later analysis.

The points, P1P2 were determined as described above with reference toFIG. 54B. Line EF is then verified. A most posterior slice of the seriesof coronal views is chosen to verify the position of a coronal posteriorreference line, line EF. As shown in FIG. 56A, which is a coronalimaging slice of FIG. 54A, the most posterior slice 401 of the femur(e.g., slice 6 in FIGS. 54A and 56B) is chosen to position line EF suchthat line EF is generally positioned in the center of the lateral andmedial condyles 302 z, 303 z. Line EF is generally aligned with thecortical bone of the undamaged posterior condyle. For example, if themedial condyle 303 z is damaged, the line EF will be aligned with theundamaged lateral condyle, and vice versa. To verify the location ofline EF and as can be understood from FIG. 53, the line EF will alsoconnect the most posterior points P1, P2. The geography information ofline EF will be stored for future analysis.

In some embodiments, line HO may be determined. As shown in FIG. 56B,which are coronal imaging slices 6-9 taken along section lines 6-9 ofthe femur in FIG. 54A, each of the image slices 6-9 taken from FIG. 54Acan be aligned along the trochlear groove. The points H6, H7, H8, H9respectively represent the lowest extremity of the trochlear groove foreach of the image slices 6-8 from FIG. 54A. By connecting the variouspoints H6, H7, H8, the point 0 can again be obtained. The resulting lineHO is established as the shaft reference line-line SHR. Thecoronal-posterior reference line, line EF and coronal-distal referenceline, line AB may be adjusted to be perpendicular or nearlyperpendicular to the shaft reference line-line SHR (line HO). Thus, theshaft reference line, line SHR (line HO) is perpendicular or nearlyperpendicular to the coronal-posterior reference line, line EF and tothe coronal-distal reference line, line AB throughout the coronal imageslices.

As can be understood from FIGS. 53 and 56B, the trochlear grooveplane-GHO, as the reference across the most distal extremity of thetrochlear groove of the femur and in a 90° knee extension, should beperpendicular to line AB. The line-HO, as the reference across the mostposterior extremity of trochlear groove of the femur and in a 0° kneeextension, should be perpendicular to line AB.

e. Determining Reference Line AB and Reference Planes P and S

As can be understood from FIG. 53, a posterior plane S may beconstructed such that the plane S is normal to line GO and includesposterior reference points P1, P2. A distal plane P may be constructedsuch that it is perpendicular to posterior plane S and may includedistal reference points D1, D2 (line CD). Plane P is perpendicular toplane S and forms line AB therewith. Line HO and line GO areperpendicular or nearly perpendicular to each other. Lines CD, AB and EFare parallel or nearly parallel to each other. Lines CD, AB and EF areperpendicular or nearly perpendicular to lines HO and GO and thetrochlear plane GHO.

f. Verification of the Femoral Reference Data

As shown in FIG. 56C, which is an imaging slice of the femur of FIG. 54Ain the sagittal view, after the establishment of the reference linesfrom the axial and coronal views, the axial-distal reference line CD andcoronal-posterior reference line EF and planes P, S are verified in the2D sagittal view. The sagittal views provide the extension/flexionadjustment. Thus, as shown in FIG. 56C, slice 800 z shows a sagittalview of the femoral medial condyle 303 z. Line-bf and line-bd intersectat point-b. As can be understood from FIGS. 53 and 56C, line-bf falls onthe coronal plane-S, and line-bd falls on the axial plane-P. Thus, inone embodiment of POP planning, axial and coronal views are used togenerate axial-distal and coronal-posterior reference lines CD, EF.These two reference lines CD, EF can be adjusted (via manipulation ofthe reference data once it has been imported and opened on the computer)to touch in the black cortical rim of the femur. The adjustment of thetwo reference lines on the femur can also be viewed simultaneously inthe sagittal view of the MRI slice, as displayed in FIG. 56C. Thus, thesagittal view in FIG. 56C provides one approach to verify if the tworeference lines do touch or approximately touch with the femur corticalbone. In some embodiments, line-bf is perpendicular or nearlyperpendicular to line-bd. In other embodiments, line bf may not beperpendicular to bd. This angle depends at least partially on therotation of femoral bone within MRI.

With reference to FIGS. 53-56C, in one embodiment, lines HO and GO maybe within approximately six degrees of being perpendicular with linesP1P2, D1D2 and A1A2 or the preoperative planning for the distal femurwill be rejected and the above-described processes to establish thefemoral reference data 100 z (e.g. reference lines CD, EF, AB, referencepoints P1P2, D1D2) will be repeated until the femoral reference datameets the stated tolerances, or a manual segmentation for setting up thereference lines will be performed. In other embodiments, if there aremultiple failed attempts to provide the reference lines, then thereference data may be obtained from another similar joint that issufficiently free of deterioration. For example, in the context ofknees, if repeated attempts have been made without success to determinedreference data in a right knee medial femur condyle based on dataobtained from the right knee lateral side, then reference data could beobtained from the left knee lateral or medial sides for use in thedetermination of the femoral reference data.

g. Mapping the Femoral Reference Data to a Y-Z Plane

As can be understood from FIGS. 56D-58, the femoral reference data 100 zwill be mapped to a y-z coordinate system to aid in the selection of anappropriate implant. As shown in FIGS. 56D-56E, which are axial andcoronal slices, respectively, of the femur, the points D1D2 of thedistal reference line D1D2 or CD were determined from both a 2D axialview and a 2D coronal view and therefore are completely defined in 3D.Similarly, the points P1P2 of the posterior reference line P1P2 or EFwere determined from both a 2D axial view and a 2D coronal view andtherefore are completely defined in 3D.

As shown in FIG. 57, which is a posterior view of a femur 3D model 1000z, the reference data 100 z determined by an analysis of 2D images maybe imported onto a 3D model of the femur for verification purposes. Thedistance L between line EF and line CD can be determined and stored forlater analysis during the selection of an appropriate implant size.

As indicated in FIG. 58, which depicts a y-z coordinate system, theposterior points P1P2 and distal points D1D2 of the 2D images 28′ mayalso be projected onto a y-z plane and this information is stored forlater analysis.

2. Determining Femoral Implant Reference Data

There are 6 degrees of freedom for a femoral implant to be moved androtated for placement on the femoral bone. The femur reference data 100z (e.g. points P1P2, D1D2, reference lines EF, CD, reference planes P,S) is utilized in the selection and placement of the femoral implant.For a discussion of a process used to determine the implant referencedata, reference is now made to FIGS. 59-71.

a. Map Femur Reference Data to Implant Model to Establish FemoralImplant Reference Data

As shown in FIGS. 59 and 60, which are perspective views of a femoralimplant model 34′, the femur reference data 100 z may be mapped to a 3Dmodel of the femur implant model 34′ in a process of POP. The femurreference data 100 z and the femur implant model 34′ are openedtogether. The femur implant model 34′ is placed on a 3D coordinatesystem and the data 100 z is also transferred to that coordinate systemthereby mapping the data 100 z to the model 34′ to create femoralimplant data 100 z′. The femoral implant data 100 z′ includes anaxial-distal reference line (line-C′D′) and a coronal-posteriorreference line (line-E′F′).

As can be understood from FIGS. 59 and 60, distal line-D1′D2′ representsthe distance between the two most distal points D1′, D2′. Posteriorline—P1′P2′ represents the distance between the two most posteriorpoints P1′, P2′. The lines—D1′D2′ P1′P2′ of the implant model 34′ can bedetermined and stored for further analysis.

As shown in FIG. 61, which shows a coordinate system wherein some of thefemoral implant reference data 100 z′ is shown, the endpoints D1′D2′ andP1′P2′ may also be projected onto a y-z plane and this information isstored for later analysis. As shown in FIG. 62, the implant referencedata 100 z′ may also be projected onto the coordinate system. Thedistance L′ between line E′F′ and line C′D′, and more specificallybetween lines D1′D2′, P1′P2′ can be determined and stored for later useduring the selection of an implant.

3. Determining Joint Line and Adjustment to Implant That Allows CondylarSurfaces of Implant Model to Restore Joint to Natural Configuration

In order to allow an actual physical arthroplasty implant to restore thepatient's knee to the knee's pre-degenerated or natural configurationwith the natural alignment and natural tensioning in the ligaments, thecondylar surfaces of the actual physical implant generally replicate thecondylar surfaces of the pre-degenerated joint bone. In one embodimentof the systems and methods disclosed herein, condylar surfaces of the 2Dimplant model 34′ are matched to the condylar surfaces of the 2D bonemodel or image 28′. However, because the bone model 28′ may be bone onlyand not reflect the presence of the cartilage that actually extends overthe pre-degenerated condylar surfaces, the alignment of the implant 34′may be adjusted to account for cartilage or proper spacing between thecondylar surfaces of the cooperating actual physical implants (e.g., anactual physical femoral implant and an actual physical tibia implant)used to restore the joint such that the actual physical condylarsurfaces of the actual physical cooperating implants will generallycontact and interact in a manner substantially similar to the way thecartilage covered condylar surfaces of the pre-degenerated femur andtibia contacted and interacted. Thus, in one embodiment, the implantmodels are modified or positionally adjusted to achieve the properspacing between the femur and tibia implants.

a. Determine Adjustment Value Tr

To achieve the correct adjustment, an adjustment value tr may bedetermined. In one embodiment, the adjustment value tr may be determinedin 2D by a calipers measuring tool (a tool available as part of thesoftware). The calipers tool is used to measure joint spacing betweenthe femur and the tibia by selection of two points in any of the 2D MRIviews and measuring the actual distance between the points. In anotherembodiment, the adjustment value tr that is used to adjust the implantduring planning may be based off of an analysis associated withcartilage thickness. In another embodiment, the adjustment value tr usedto adjust the implant during planning may be based off of an analysis ofproper joint gap spacing. Both the cartilage thickness and joint gapspacing methods are discussed below in turn.

i. Determining Cartilage Thickness and Joint Line

FIG. 63A shows the femoral condyle 310 z and the proximal tibia of theknee in a sagittal MRI image slice. The distal femur 28′ is surroundedby the thin black rim of cortical bone. Due to the nature of irregularbone and cartilage loss in OA patients, it can be difficult to find theproper joint line reference for the models used during the POP.

The space between the elliptical outlining 325 z′, 325 z″ along thecortical bone represents the cartilage thickness of the femoral condyle310 z. The ellipse contour of the femoral condyle 310 z can be seen onthe MRI slice shown in FIG. 63A and obtained by a three-point tangentcontact spot (i.e., point t1, t2, t3) method. In a normal, healthy knee,the bone joint surface is surrounded by a layer of cartilage. Becausethe cartilage is generally worn-out in OA and the level of cartilageloss varies from patient to patient, it may be difficult to accuratelyaccount for the cartilage loss in OA patients when trying to restore thejoint via TKA surgery. Therefore, in one embodiment of the methodologyand system disclosed herein, a minimum thickness of cartilage isobtained based on medical imaging scans (e.g., MRI, etc.) of theundamaged condyle. Based on the cartilage information, the joint linereference can be restored. For example, the joint line may be line 630 zin FIG. 63B.

The system and method disclosed herein provides a POP method tosubstantially restore the joint line back to a “normal or natural knee”status (i.e., the joint line of the knee before OA occurred) andpreserves ligaments in TKA surgery (e.g., for a total knee arthroplastyimplant) or partial knee arthroplasty surgery (e.g., for a uni-kneeimplant).

FIG. 63B is a coronal view of a knee model in extension. As depicted inFIG. 63B, there are essentially four separate ligaments that stabilizethe knee joint, which are the medial collateral ligament (MCL), anteriorcruciate ligament (ACL), lateral collateral ligament (LCL), andposterior cruciate ligament (PCL). The MCL and LCL lie on the sides ofthe joint line and serve as stabilizers for the side-to-side stabilityof the knee joint. The MCL is a broader ligament, whereas the LCL is adistinct cord-like structure.

The ACL is located in the front part of the center of the joint. The ACLis a very important stabilizer of the femur on the tibia and serves toprevent the tibia from rotating and sliding forward during agility,jumping, and deceleration activities. The PCL is located directly behindthe ACL and serves to prevent the tibia from sliding to the rear. Thesystem and method disclosed herein provides POP that allows thepreservation of the existing ligaments without ligament release duringTKA surgery. Also, the POP method provides ligament balance, simplifyingTKA surgery procedures and reducing pain and trauma for OA patients.

As indicated in FIG. 63B, the joint line reference 630 z is definedbetween the two femoral condyles 302 z, 303 z and their correspondingtibia plateau regions 404 z, 406 z. Area A illustrates a portion of thelateral femoral condyle 302 z and a portion of the corresponding lateralplateau 404 z of tibia 205 z. Area B illustrates the area of interestshowing a portion of the medial femoral condyle 303 z and a portion ofthe corresponding medial plateau 406 z of tibia 205 z.

FIGS. 63C, 63D and 63F illustrate MRI segmentation slices for joint lineassessment. FIG. 63E is a flow chart illustrating the method fordetermining cartilage thickness used to determine proper joint line. Thedistal femur 200 z is surrounded by the thin black rim of cortical bone645 z. The cancellous bone (also called trabecular bone) 650 z is aninner spongy structure. An area of cartilage loss 655 z can be seen atthe posterior distal femur. For OA patients, the degenerative cartilageprocess typically leads to an asymmetric wear pattern that results inone femoral condyle with significantly less articulating cartilage thanthe other femoral condyle. This occurs when one femoral condyle isoverloaded as compared to the other femoral condyle.

As can be understood from FIGS. 63C, 63E and 63F, the minimum cartilagethickness is observed and measured for the undamaged and damaged femoralcondyle 302 z, 303 z [block 1170]. If the greatest cartilage loss isidentified on the surface of medial condyle 303 z, for example, then thelateral condyle 302 z can be used as the cartilage thickness referencefor purposes of POP. Similarly, if the greatest cartilage loss isidentified on the lateral condyle 302 z, then the medial condyle 303 zcan be used as the cartilage thickness reference for purposes of POP. Inother words, use the cartilage thickness measured for the least damagedcondyle cartilage as the cartilage thickness reference for POP [block1175].

As indicated in FIG. 63D, the thickness of cartilage can be analyzed inorder to restore the damaged knee compartment back to its pre-OA status.In each of the MRI slices taken in regions A and B in FIG. 63B, thereference lines as well as the major and minor axes 485 z, 490 z ofellipse contours 480 z′, 480 z″ in one femoral condyle 303 z can beobtained.

As shown in FIG. 63F, for the three-point method, the tangents are drawnon the condylar curve at zero degrees and 90 degrees articular contactpoints. The corresponding tangent contact spots t1 and t2 are obtainedfrom the tangents. The line 1450 z perpendicular to the line 1455 zdetermines the center of the ellipse curve, giving the origin of (0, 0).A third tangent contact spot t3 can be obtained at any point along theellipse contour between the zero degree, t1 point and the 90 degree, t2point. This third spot t3 can be defined as k, where k=1 to n points.

The three-point tangent contact spot analysis may be employed toconfigure the size and radius of the condyle 303 z of the femur bonemodel 28′. This provides the “x” coordinate and “y” coordinate, as the(x, y) origin (0, 0) shown in FIG. 63D. The inner ellipse model 480 z′of the femoral condyle shows the femoral condyle surrounded by corticalbone without the cartilage attached. The minimum cartilage thicknesstmmin outside the inner ellipse contour 480 z′ is measured. Based on theanalysis of the inner ellipse contour 480 z′ (i.e., the bone surface)and outer ellipse contour 480 z″ (i.e., the cartilage surface) of theone non-damaged condyle of the femur bone model 28′, the inner ellipsecontour 480 z′ (i.e., the bone surface) and the outer ellipse contour480 z″ (i.e., the cartilage surface) of the other condyle (i.e., thedamage or deteriorated condyle) may be determined.

As can be understood from FIGS. 63B and 63D, ellipse contours 480 z′,480 z″ are determined in areas A and B for the condyles 302 z, 303 z ofthe femur bone model 28′. The inner ellipse contour 480 z′, representingthe bone-only surface, and the outer ellipse contour 480 z″,representing the bone-and-cartilage surface, can be obtained. Theminimum cartilage thickness tmmin is measured based on the cartilagethickness tr between the inner ellipse 480 z′ and outer ellipse 480 z″.MRI slices of the two condyles 302 z, 303 z of the femur bone model 28′in areas A and B are taken to compare the respective ellipse contours inareas A and B. If the cartilage loss is greatest at the medial condyle303 z in the MRI slices, the minimum thickness tmmin for the cartilagecan be obtained from the lateral condyle 302 z. Similarly, if thelateral condyle 302 z has the greatest cartilage loss, the cartilagethickness tmmin can be obtained from undamaged medial condyle 303 z ofthe femur restored bone model 28′. The minimum cartilage can beillustrated in the formula, tmmin=MIN (ti), where i=1 to k.

ii. Determining Joint Gap

As mentioned above, in one embodiment, the adjustment value tr may bedetermined via a joint line gap assessment. The gap assessment may serveas a primary estimation of the gap between the distal femur and proximaltibia of the bone images. The gap assessment may help achieve properligament balancing.

In one embodiment, an appropriate ligament length and joint gap may notbe known from the 2D bone models or images 28′, 28″ (see, e.g. FIG. 52B)as the bone models or images may be oriented relative to each other in afashion that reflects their deteriorated state. For example, as depictedin FIG. 63J, which is a coronal view of bone models 28′, 28″ oriented(e.g., tilted) relative to each other in a deteriorated stateorientation, the lateral side 1487 z was the side of the deteriorationand, as a result, has a greater joint gap between the distal femur andthe proximal tibia than the medial side 1485 z, which was thenon-deteriorated side of the joint in this example.

In one embodiment, ligament balancing may also be considered as a factorfor selecting the appropriate implant size. As can be understood fromFIG. 63J, because of the big joint gap in the lateral side 1487 z, thepresumed lateral ligament length (L1+L2+L3) may not be reliable todetermine proper ligament balancing. However, the undamaged side, whichin FIG. 63J is the medial side 1485 z, may be used in some embodimentsas the data reference for a ligament balancing approach. For example,the medial ligament length (M1+M2+M3) of the undamaged medial side 1485z may be the reference ligament length used for the ligament balancingapproach for implant size selection.

In one embodiment of the implant size selection process, it may beassumed that the non-deteriorated side (i.e., the medial side 1485 z inFIG. 63J in this example) may have the correct ligament length forproper ligament balancing, which may be the ligament length of(M1+M2+M3). When the associated ligament length (“ALL”) associated witha selected implant size equals the correct ligament length of(M1+M2+M3), then the correct ligament balance is achieved, and theappropriate implant size has been selected. However, when the ALL endsup being greater than the correct ligament length (M1+M2+M3), theimplant size associated with the ALL may be incorrect and the nextlarger implant size may need to be selected for the design of thearthroplasty jig 2.

For a discussion regarding the gap assessment, which may also be basedon ligament balance off of a non-deteriorated side of the joint,reference is made to FIGS. 63G and 63H. FIGS. 63G and 63H illustratecoronal views of the bone models 28′, 28″ in their post-degenerationalignment relative to each as a result of OA or injury. As shown in FIG.63G, the tibia model 28″ is tilted away from the lateral side 1487 z ofthe knee 1486 z such that the joint gap between the femoral condylarsurfaces 1490 z and the tibia condylar surfaces 1491 z on the lateralside 1487 z is greater than the joint gap on the medial side 1485 z.

As indicated in FIG. 63H, which illustrates the tibia in a coronal crosssection, the line 1495 z may be employed to restore the joint line ofthe knee 1486 z. The line 1495 z may be caused to extend across each oflowest extremity points 1496 z, 1497 z of the respective femoral lateraland medial condyles 1498 z, 1499 z. In this femur bone model 28′, line1495 z may be presumed to be parallel or nearly parallel to the jointline of the knee 1486 z.

As illustrated in FIG. 63H, the medial gap Gp2 represents the distancebetween the distal femoral medial condyle 1499 z and the proximal tibiamedial plateau 1477 z. The lateral gap Gp1 represents the distancebetween the distal femoral lateral condyle 1498 z and the proximal tibialateral plateau 1478 z. In this example illustrated in FIG. 63H, thelateral gap Gp1 is significantly larger than the medial gap Gp2 due todegeneration caused by injury, OA, or etc., that occurred in the lateralside 1487 z of the knee 1486 z. It should be noted that the alignment ofthe bone models 28′, 28″ relative to each other for the exampleillustrated in FIGS. 63G and 63H depict the alignment the actual boneshave relative to each other in a deteriorated state. To restore thejoint line reference and maintain ligament balancing for the medialcollateral ligament (MCL) and lateral collateral ligament (LCL), thejoint line gap Gp3 that is depicted in FIG. 63I, which is the same viewas FIG. 63G, except with the joint line gap Gp3 in a restored state, maybe used for the joint spacing compensation adjustment as describedbelow. As illustrated in FIG. 63I, the lines 1495 z and 1476 zrespectively extend across the most distal contact points 1496 z, 1497 zof the femur condyles 1498 z, 1499 z and the most proximal contactpoints 1466 z, 1467 z of the tibia plateau condyles 1477 z, 1478 z.

For calculation purposes, the restored joint line gap Gp3 may bewhichever of Gp1 and Gp2 has the minimum value. In other words, therestored joint line gap Gp3 may be as follows: Gp3=MIN (Gp1, Gp2). Forpurposes of the adjustment for joint spacing compensation, theadjustment value tr may be calculated as being half of the value forGp3, or in other words, tr=Gp3/2. As can be understood from FIGS.63G-63H and 14J, in this example, the non-deteriorated side 1485 z hasGp2, which is the smallest joint line gap and, therefore, Gp3=Gp2 in theexample depicted in FIG. 63G-14J, and tr=Gp2/2.

In one embodiment, the joint line gap assessment may be at least a partof a primary assessment of the geometry relationship between the distalfemur and proximal tibia. In such an embodiment, the joint gapassessment step may occur prior to the femur planning steps of the POPprocess. However, in other embodiments, the joint line gap assessmentmay occur at other points along the overall POP process.

b. Determine Compensation for Joint Spacing

Once the adjustment value tr is determined based off of cartilagethickness or joint line gap Gp3, the planning for the femoral implantmodel 34′ can be modified or adjusted to compensate for the jointspacing in order to restore the joint line. As shown in FIG. 64, whichis a 3D coordinate system wherein the femur reference data 100 z isshown, the compensation for the joint spacing is performed both indistal and posterior approaches. Thus, the joint compensation pointsrelative to the femur reference data are determined. As will bediscussed later in this Detailed Description, the joint compensationpoints relative to the femur reference data will be used to determinethe joint compensation relative to the femur implant.

As can be understood from FIG. 65, which is a y-z plane wherein thejoint compensation points are shown, the posterior plane S and thedistal plane P are moved away in the direction of normal of plane S andP respectively by the adjustment value tr. In one embodiment, theadjustment value tr is equal to the cartilage thickness. That is, thejoint compensation points will be determined relative to the posteriorplane S and the distal plane P which are moved away in the direction ofnormal of plane S and P, respectively, by an amount equal to thecartilage thickness. In some embodiments, the adjustment value tr isequal to one-half of the joint spacing. That is, the joint compensationpoints will be determined relative to the posterior plane S and thedistal plane P which are moved away in the direction of normal of planeS and P, respectively, by an amount equal one-half the joint spacing. Inother words, the femoral implant accounts for half of the joint spacingcompensation, while the tibia implant will account for the other half ofthe joint spacing compensation.

As can be understood from FIG. 64, the femur reference data 100 z wasuploaded onto a coordinate system, as described above. To compensate forthe joint spacing, the distal line-D1D2 is moved closer to the distalplane-P by an amount equal to the adjustment value tr, thereby resultingin joint spacing compensation points D1J, D2J and line D1JD2J. Thedistal plane P was previously moved by adjustment value tr. Similarly,posterior reference line P1P2 is moved closer to the posterior plane-Sby an amount equal to the adjustment value tr, thereby resulting injoint spacing compensation points P1J, P2J and line P1JP2J. Thetrochlear groove reference line-line GO does not move and remains as thereference line for the joint spacing compensation. Lines D1JD2J andP1JP2J will be stored and utilized later for an analysis related to thefemoral implant silhouette curve.

4. Selecting the Sizes for the Femoral Implants

The next steps are designed to select an appropriate implant size suchthat the implant will be positioned within the available degrees offreedom and may be optimized by 2D optimization. There are 6 degrees offreedom for a femoral implant to be moved and rotated for placement onthe femur. For example, the translation in the x direction is fixedbased on the reference planes-S and P and sagittal slices of femur asshown in FIGS. 53 and 63C. Rotation around the y axis, which correspondsto the varus/valgus adjustment is fixed based on the reference linesdetermined by analysis of the coronal slices, namely, lines EF and ABand coronal plane-S as shown in FIGS. 53 and 56B. Rotation around the zaxis, which corresponds to internal/external rotation, is fixed by thetrochlear groove reference line, line GO or TGB, axial-distal referenceline, line CD, and axial-posterior reference line, line AB as shown inthe axial views in FIGS. 53 and 55A-55E. By fixing these three degreesof freedom, the position of the implant can be determined so that theouter silhouette line of the implant passes through both the distalreference line and posterior reference line. Optimization will searchfor a sub-optimal placement of the implant such that an additional angleof flange contact is greater than but relatively close to 7 degrees.Thus, by constraining the 3 degrees of freedom, the appropriate implantcan be determined.

a. Overview of Selection of Femoral Implant

Based on previously determined femoral implant data 100 z′, as shown inFIGS. 60-62, a set of 3 possible sizes of implants are chosen. For eachimplant, the outer 2D silhouette curve of the articular surface of thecandidate implant model is computed and projected onto a y-z plane, asshown in FIGS. 69A-69C. The calculated points of the silhouette curveare stored. Then, the sagittal slice corresponding to the inflectionpoint 500 z (see FIG. 70A) is found and the corresponding segmentationspline is considered and the information is stored. Then an iterativeclosest point alignment is devised to find the transform to match theimplant to the femur.

The next sections of this Detailed Description will now discuss theprocess for determining the appropriate implant candidate, withreference to FIGS. 66-71.

i. Implant Selection

In one embodiment, there is a limited number of sizes of a candidatefemoral implant. For example, one manufacturer may supply six sizes offemoral implants and another manufacturer may supply eight or anothernumber of femoral implants. A first implant candidate 700 z (see FIG.66) may be chosen based on the distance L′ between the posterior anddistal reference lines P1′P2′ and D1′D2′ determined above in FIG. 62,with reference to the femoral implant reference data 100 z′. Thedistance L′ of the candidate implants may be stored in a database andcan be retrieved from the implant catalogue. In some embodiments, asecond and third implant candidate 702, 704 (not shown) may be chosenbased on the distance L between the posterior and distal reference linesP1P2 and D1D2 of the femur 28′ determined above in FIG. 57, withreference to the femoral reference data 100 z and distance L′. Firstimplant candidate 700 z has the same distance L as the patient femur.Second implant candidate 702 is one size smaller than the first implantcandidate 700 z. Third implant candidate 704 is one size larger than thefirst implant candidate 700 z. In some embodiments, more than 3 implantcandidates may be chosen.

The following steps 2-6 are performed for each of the implant candidates700 z, 702, 704 in order to select the appropriate femoral implant 34′.

ii. Gross Alignment of Implant onto Femur

In some embodiments, the gross alignment of the implant 34′ onto thefemur 28′ may be by comparison of the implant reference data 100 z′ andthe femur reference data 100 z. In some embodiments, gross alignment maybe via comparison of the medial-lateral extents of both the implant andthe femur. In some embodiments, both gross alignment techniques may beused.

In some embodiments, as shown in FIG. 66, which shows the implant 34′placed onto the same coordinate plane with the femur reference data 100z, the implant candidate may be aligned with the femur. Alignment withthe femur may be based on the previously determined implant referencelines D1′D2′ and P1′P2′ and femur reference lines D1D2 and P1P2.

In some embodiments, and as can be understood from FIGS. 67A-67C and68A-68C, the medial lateral extent of the femur and the implant can bedetermined and compared to ensure the proper initial alignment. FIG. 67Ais a plan view of the joint side 240 z of the femur implant model 34′depicted in FIG. 52B. FIG. 67B is an axial end view of the femur lowerend 200 z of the femur bone model 28′ depicted in FIG. 52A. The viewsdepicted in FIGS. 67A and 67B are used to select the proper size for thefemoral implant model 34′.

As can be understood from FIG. 67A, each femoral implant available viathe various implant manufacturers may be represented by a specificfemoral implant 3D computer model 34′ having a size and dimensionsspecific to the actual femoral implant. Thus, the representative implantmodel 34′ of FIG. 67A may have an associated size and associateddimensions in the form of, for example, an anterior-posterior extent iAPand medial-lateral extent iML, which data can be computed and stored ina database. These implant extents iAP, iML may be compared to thedimensions of the femur slices from the patient's actual femur 18. Forexample, the femur bone 18 may have dimensions such as, for example, ananterior-proximal extent bAP and a medial-lateral extent bML, as shownin FIG. 67B. In FIG. 67A, the anterior-posterior extent iAP of thefemoral implant model 34′ is measured from the anterior edge 270 z tothe posterior edge 275 z of the femoral implant model 34′, and themedial-lateral extent iML is measured from the medial edge 280 z to thelateral edge 285 z of the femoral implant model 34′.

Each patient has femurs that are unique in size and configuration fromthe femurs of other patients. Accordingly, each femur slice will beunique in size and configuration to match the size and configuration ofthe femur medically imaged. As can be understood from FIG. 67B, thefemoral anterior-posterior length bAP is measured from the anterior edge290 z of the patellofemoral groove to the posterior edge 295 of thefemoral condyle, and the femoral medial-lateral length bML is measuredfrom the medial edge 301 z of the medial condyle to the lateral edge 305z of the lateral condyle. The implant extents iAP and iML and the femurextents bAP, bML may be aligned for proper implant placement as shown inFIG. 67C and along the direction of axial-distal reference line-CD.

As can be understood from FIGS. 68A-68C, these medial-lateral extents ofthe implant iML and femur bML can be measured from the 2D slices of thefemur of FIG. 54A. For example, FIG. 68A, which shows the most medialedge of the femur in a 2D sagittal slice and FIG. 68B, which shows themost lateral edge of the femur in a 2D sagittal slice, can be used tocalculate the bML of the femur 28′. The implant 34′ will be centeredbetween the medial and lateral edges, as shown in FIG. 68C, which is a2D slice in coronal view showing the medial and lateral edges, therebygrossly aligning the implant with the femur.

iii. Determine Outer Silhouette Curve of Implant in Y-Z Plane

The silhouette of the femoral implant is the curve formed by farthestpoints from center in y-z plane projection of the femoral implantgeometry. The points of the silhouette curve may be utilized to confirmplacement of the implant onto the femur based on the femur referencelines that have been altered to account for the joint compensation.

For a discussion of the process for determining the points of thesilhouette curve of the femoral implant, reference is now made to FIGS.69A-69C. As can be understood from FIG. 69A, which is an implant 34′mapped onto a y-z plane, the points of a candidate implant are retrievedfrom the implant database. The points are then imported onto a y-z planeand the silhouette curve can be determined. The silhouette curve 34″ isdetermined by finding the points that are the farthest from the centeralong an outer circumference 35 of the articular surface of the implant34′. FIG. 69B, which is the silhouette curve 34″ of the implant 34′,shows the result of the silhouette curve calculations. The silhouettecurve data is then imported into a y-z plane that includes the jointspacing compensation data, as shown in FIG. 69C, which is the silhouettecurve 34″ aligned with the joint spacing compensation points D1JD2J andP1JP2J. The resulting joint spacing compensation and silhouette curvedata 800 z (e.g. D1′″D2′″P1′″P2′″) is stored for later analysis.

iv. Determination of Inflection Point, Flange Point, Femur Spline andAnterior Femur Cut Plane

The flange point is determined and stored. As can be understood fromFIG. 70A, which shows a distal femur 28′ with an implant 34′, the distalfemur is analyzed and the flange point 500 z of the implant 34′ isdetermined relative to the anterior surface 502 z of the distal end of afemur condyle 28′. FIG. 70B, which depicts a femur implant 34′,illustrates the location of the flange point 500 z on the implant 34′ asdetermined by an analysis such as one illustrated in FIG. 70A.

The anterior cut plane 504 z is determined and stored. The range of theanterior cut plane of the implant is determined such that the cut plane(and therefore the implant) is within certain tolerances. As shown inFIG. 70A, a cut plane 504 z is determined based on the location of theimplant 34′ on the femur 28′. An angle A between the cut plane 504 z andthe flange point 500 z is between approximately 7 and approximately 15degrees. In some embodiments, the angle A is approximately 7 degrees. Insome embodiments, the distal cut plane may be found as described belowwith respect to the final verification step. For each respectiveimplant, the anterior cut plane and the distal cut plane are at a fixedangle for the implant. That is, once the anterior cut plane is found,the distal cut plane can be determined relative to the fixed angle andthe anterior cut plane. Alternatively, once the distal cut plane isfound, the anterior cut plane can be determined relative to the fixedangle and the distal cut plane.

The inflection point 506 z is determined and stored. As shown in FIG.70C, which shows a slice of the distal femur 28′ in the sagittal view,the inflection point 506 z is located on the anterior shaft of thespline 508 of femur 28′ where the flange point 500 z of the implant 34′is in contact with the femur 28′. An implant matching algorithm willmatch the flange point 500 z of implant 34′ to the spline 508 of thefemur at approximately the inflection point 506 z of the femur 28′. Ascan be understood from FIG. 70D, which shows the implant 34′ positionedon the femur 28′, the implant 34′ should be aligned to touch the distaland posterior reference planes P, S respectively to reach properalignment. In one embodiment, the implant matching algorithm is acustomized extension of an algorithm known as iterative closest pointmatching.

The next section of the Detailed Description now discusses how the dataand data points determined above and stored for future analysis will beused in the selection of an appropriate implant.

v. Determine Points of Set A and Set B

Determination of the data sets contained in Set A and Set B aid indetermining the appropriate implant and ensuring that the chosen implantmates with the femur within certain tolerances.

The joint spacing compensation points D1JD2J and P1JP2J were determinedas described with reference to FIG. 65 and are added to Set A. Next, thejoint spacing compensation points D1JD2J and P1JP2J are matched to theclosest respective points on the silhouette curve, as shown in FIG. 69C,thereby resulting in points D1′″D2′″ and P1′″P2′″ or the joint spacingcompensation and silhouette curve data 800 z. Points D1′″D2′″ andP1′″P2′″ will be added to Set B.

The inflection point and flange point data are analyzed. An inflectionpoint 506 z′ is found to represent the inflection point 506 z that isclosest in proximity to the flange point 500 z, which were bothdiscussed with reference to FIGS. 70A-70D. The point 506 z′ is added toSet A. The flange point 500 z is then projected to a y-z plane. Theresulting flange point 500 z′ is added to Set B.

Thus, Set A contains the following points: the joint spacingcompensation points D1JD2J and P1JP2J and the inflection point 506 z′.Set B contains the following points: Points D1′″D2′″ and P1′″P2′″ (thejoint spacing compensation and silhouette curve data 800 z) and theflange point 500 z′.

vi. Utilize the Data of Sets A and B

Find a rigid body transform. The data points of Set A and Set B arecompared and a rigid body transform that most closely matches Set A toSet B is chosen. The rigid body transform will transform an objectwithout scaling or deforming. That is, the rigid body transform willshow a change of position and orientation of the object. The chosentransform will have rotation about the x-axis and translation in the y-zplane.

Find the inverse of the rigid body transform. The inverse of this rigidbody transform is then imported into the y-z plane that also containsthe femur reference lines D1D2 and P1P2 and the femur spline 508 thatcorresponds to the flange point 500 z of the implant 34′.

The steps described in this Detailed Description are repeated until therelative motion is within a small tolerance. In one embodiment, thesteps are repeated fifty times. In some embodiments, the steps arerepeated more than fifty times or less than fifty times.

In some embodiments, and as shown in FIG. 71A, an acceptable translationin y-z plane may be determined. FIG. 71A depicts an implant that isimproperly aligned on a femur, but shows the range of the search for anacceptable angle A. Within this range for angle A, the translation iny-z leads to finding the rigid body transform as described above. Insome embodiments, the process may optimize y-z translation and rotationaround the x-axis in one step. This can be done by rotating the implantsilhouette curve by several half degree increments and then, for eachincrement, performing the steps described in this Detailed Description.Translation in the y-z axis only occurs during the analysis utilizingthe inverse of the rigid body transform.

vii. Additional Verification and Confirmation of Femur Cut Plane

By using the above outlined procedure, an appropriate implant is foundby choosing the implant and transform combination that provides aninflection angle that is greater than 7 degrees but closest to 7degrees, as explained with reference to FIG. 70A.

In some embodiments, an additional verification step is performed byplacing the implant 34′ in the MRI with the transform 28′″ that is foundby the above described method. As can be understood from FIG. 71B, whichillustrates the implant positioned on the femur transform wherein afemur cut plane is shown, during the verification step, a user maydetermine the amount of bone that is cut J1 on the medial and lateralcondyles by looking at the distal cut plane 514 z of the implant 34′. J1is determined such that the thickness of the bone cut on both the medialand lateral sides is such that the bone is flat after the cut. Multipleslices in both the distal and medial areas of the bone can be viewed toverify J1 is of proper thickness.

Once an appropriate femur implant is chosen, the preoperative planningprocess turns to the selection of an appropriate tibia implant. Thetibia planning process includes a determination of the tibia referencelines to help determine the proper placement of the tibia implant. Thecandidate tibia implant is placed relative to the tibia reference linesand placement is confirmed based on comparison with several 2Dsegmentation splines.

E. Tibia Planning Process

For a discussion of the tibia planning process, reference is now made toFIGS. 72-81D. FIGS. 72-75B illustrate a process in the POP wherein thesystem 10 utilizes 2D imaging slices (e.g., MRI slices, CT slices, etc.)to determine tibia reference data, such as reference points andreference lines, relative to the undamaged side of the tibia plateau.The resulting tibia reference data 900 z is then mapped or projected toan x-y plane (axial plane). A candidate tibia implant is chosen, whichselection will be discussed with reference to FIGS. 76A-76C. The tibiaimplant placement is adjusted and confirmed relative to the tibia, asdiscussed in more detail below with reference to FIGS. 77-81D.

1. Determining Tibia Reference Data

For a discussion of a process used to determine the tibia reference data900 z, reference is now made to FIGS. 72-76B. As can be understood fromFIG. 72, which is a top view of the tibia plateaus 404 z, 406 z of atibia bone image or model 28″, the tibia reference data 900 z mayinclude reference points (e.g. Q1, Q1′), reference lines (e.g. T1T2, V1)and a reference plane (e.g. S′) (see FIGS. 75A-75B). In someembodiments, the tibia reference data 900 z may also include theanterior-posterior extant (tAP) and the medial-lateral extant (tML) ofthe tibia 28″ (see FIGS. 76A-76B). As shown in FIG. 72, each tibiaplateau 404 z, 406 z includes a curved recessed condyle contactingsurface 421 z, 422 z that is generally concave extendinganterior/posterior and medial/lateral. Each curved recessed surface 421z, 422 z is generally oval in shape and includes an anterior curved edge423 z, 424 z and a posterior curved edge 425 z, 426 z that respectivelygenerally define the anterior and posterior boundaries of the condylecontacting surfaces 421 z, 422 z of the tibia plateaus 404 z, 406 z.Depending on the patient, the medial tibia plateau 406 z may have curvededges 424 z, 426 z that are slightly more defined than the curved edges423 z, 425 z of the lateral tibia plateau 404 z.

a. Identify Points Q1, Q2 and Q1′, Q2′

2D slices in the sagittal view are analyzed to determine the tibiaflexion/extension adjustment. Anterior tangent lines TQ1, TQ2 can beextended tangentially to the most anterior location on each anteriorcurved edge 423 z, 424 z to identify the most anterior points Q1, Q2 ofthe anterior curved edges 423 z, 424 z. Posterior tangent lines TQ1′,TQ2′ can be extended tangentially to the most posterior location on eachposterior curved edge 425 z, 426 z to identify the most posterior pointsQ1′, Q2′ of the posterior curved edges 425 z, 426 z. Thus, in oneembodiment, the lateral side tibia plateau 404 z can be analyzed viatangent lines to identify the highest points Q1, Q1′. For example,tangent line TQ1 can be used to identify the anterior highest point Q1,and tangent line TQ1′ can be used to identify the posterior highestpoint Q1′. In some embodiments, a vector V1 extending between thehighest points Q1, Q1′ may be generally perpendicular to the tangentlines TQ1, TQ1′. Similarly, the medial side tibia plateau 406 z can beanalyzed via tangent lines to identify the highest points Q2, Q2′. Forexample, tangent line TQ2 can be used to identify the anterior highestpoint Q2, and tangent line TQ2′ can be used to identify the posteriorhighest point Q2′. In some embodiments, a vector V2 extending betweenthe highest points Q2, Q2′ may be generally perpendicular to the tangentlines TQ2, TQ2′.

i. Confirm points Q1, Q2 and Q1′, Q2′

As can be understood from FIGS. 73A-73D, the location of Q1, Q1′, Q2 andQ2′ may also be confirmed by an analysis of the appropriate sagittalslice. As shown in FIG. 73A, which is a sagittal cross section through alateral tibia plateau 404 z of the tibia model or image 28′, points Q1and Q1′ can be identified as the most anterior and posterior points,respectively, of the curved recessed condyle contacting surface 421 z ofthe lateral tibia plateau 404 z. As shown in FIG. 73B, which is asagittal cross section through a medial tibia plateau 406 z of the tibiamodel 28″, points Q2 and Q2′ can be identified as the most anterior andposterior points, respectively, of the curved recessed condylecontacting surface 422 z of the medial tibia plateau 406 z. Suchanterior and posterior points may correspond to the highest points ofthe anterior and posterior portions of the respective tibia plateaus.

b. Determine Lines V1 and V2

As can be understood from FIGS. 72-73B, line V1 extends through anteriorand posterior points Q1, Q1′, and line V2 extends through anterior andposterior points Q2, Q2′. Line V1 is a lateral anterior-posteriorreference line. Line V2 is a medial posterior-anterior reference line.Each line V1, V2 may align with the lowest point of theanterior-posterior extending groove/valley that is the ellipticalrecessed tibia plateau surface 421 z, 422 z. The lowest point of theanterior-posterior extending groove/valley of the elliptical recessedtibia plateau surface 421 z, 422 z may be determined via ellipsoidcalculus. Each line V1, V2 will be generally parallel to theanterior-posterior extending valleys of its respective ellipticalrecessed tibia plateau surface 421 z, 422 z and will be generallyperpendicular to its respective tangent lines TQ1, TQ1′, TQ2, TQ2′. Theanterior-posterior extending valleys of the elliptical recessed tibiaplateau surfaces 421 z, 422 z and the lines V1, V2 aligned therewith maybe generally parallel. The planes associated with lines V1 and V2 aregenerally parallel or nearly parallel to the joint line of the kneejoint, as determined above.

Depending on the patient, the medial tibia plateau 406 z may beundamaged or less damaged than the lateral tibia plateau 404 z. In sucha case, the reference points Q2, Q2′ and reference line V2 of the medialplateau 406 z may be used to establish one or more reference points andthe reference line of the damaged lateral tibia plateau. FIG. 73Cdepicts a sagittal cross section through an undamaged or little damagedmedial tibia plateau 406 z of the tibia model 28″, wherein osteophytes432 z are also shown. As indicated in FIG. 73C, the points Q2, Q2′ canbe located on the undamaged medial plateau and set as reference points.The anterior-posterior reference line, line V2, can be constructed byconnecting the anterior and posterior reference points Q2, Q2′. Thereference line V2 from the undamaged or little damaged medial side issaved for use in determining the reference line of the lateral tibiaplateau in the case where the lateral tibia plateau is damaged. Forexample, as shown in FIG. 73D, which is a sagittal cross section througha damaged lateral tibia plateau 404 z of the tibia model 28″, theanterior point Q1 is found to be undamaged. In this case, theestablished reference line V2 from the medial plateau can be applied tothe damaged lateral plateau by aligning the reference line V2 with pointQ1. By doing so, the reference line V1 of the lateral plateau can beestablished such that line V1 touches the reference point Q1 and extendsthrough the damaged area 403 z. Thus, the reference line V1 in thelateral plateau is aligned to be parallel or nearly parallel to thereference line V2 in the medial plateau. While the above describedprocess is described in terms of extrapolating one or more referencelines of a damaged lateral plateau from an analysis of the undamagedmedial tibia plateau, it is understood that the same process can beundertaken where the lateral tibia plateau is undamaged and one or morereference lines of a damaged medial plateau can be extrapolated from thelateral tibia plateau.

In other embodiments, as can be understood from FIG. 73D and assumingthe damage to the lateral tibia plateau 404 z is not so extensive thatat least one of the highest anterior or posterior points Q1, Q1′ stillexists, the damaged tibia plateau 404 z can be analyzed via tangentlines to identify the surviving high point Q1, Q1′. For example, if thedamage to the lateral tibia plateau 404 z was concentrated in theposterior region such that the posterior highest point Q1′ no longerexisted, the tangent line TQ1 could be used to identify the anteriorhighest point Q1. Similarly, if the damage to the medial tibia plateau406 z was concentrated in the anterior region such that the anteriorhighest point Q1′ no longer existed, the tangent line TQ1′ could be usedto identify the posterior highest point Q1′. In some embodiments, avector extending between the highest points Q1, Q1′ may be generallyperpendicular to the tangent lines TQ1, TQ1′.

c. Determine Reference Points T1 and T2 and Reference Line T1T2

2D slices in both the axial and coronal views are analyzed to determinethe varus/valgus adjustment by finding the reference points T1 and T2.As shown in FIGS. 74A-74B, which are coronal and axial 2D slices of thetibia, reference points T1 and T2 are determined by an analysis of themost proximal coronal slice (FIG. 74A) and the most proximal axial slice(FIG. 74B). As indicated in FIG. 74A, in which the tibia is shown in a0° knee extension, reference points T1 and T2 are determined. The pointsT1 and T2 represent the lowest extremity of tangent contact points oneach of the lateral and medial tibia plateaus, respectively. In oneembodiment, tangent points T1 and T2 are located within the regionbetween the tibia spine and the medial and lateral epicondyle edges ofthe tibia plateau, where the slopes of tangent lines in this region aresteady and constant. For example, and as shown in FIG. 74A, the tangentpoint T1 is in the lateral plateau in Area I between the lateral side ofthe lateral intercondylar tubercle to the attachment of the lateralcollateral ligament. For the medial portion, the tangent point T2 is inArea II between the medial side of the medial intercondylar tubercle tothe medial condyle of the tibia.

As shown in FIG. 74B, the most proximal slice of the tibia in the axialview is analyzed to find reference points T1 and T2. As above, referencepoints T1 and T2 represent the lowest extremity of tangent contactpoints on each of the lateral and medial tibia plateaus. Once thereference points T1 and T2 are found in both the coronal and axialviews, a line T1T2 is found.

A line T1T2 is created by extending a line between reference points T1and T2. In some embodiments, the coronal and axial slices are viewedsimultaneously in order to align the lateral and medialanterior-posterior reference lines V1 and V2. As shown in FIG. 72, thelateral and medial anterior-posterior reference lines V1 and V2 aregenerally perpendicular or nearly perpendicular to line T1T2.

d. Determine the Approximate ACL Attachment Point (AE) and theApproximate PCL Attachment Point (PE) of the Tibia and Reference LineAEPE

As can be understood from FIGS. 72 and 74B, the reference pointsrepresenting the approximate anterior cruciate ligament (ACL) attachmentpoint of the tibia AE and the approximate posterior cruciate ligament(PCL) attachment point of the tibia PE are determined. The referencepoint AE can be determined by finding the approximate tibia attachmentpoint for the ACL. The reference point PE can be determined by findingthe approximate tibia attachment point for the PCL. The line AEPEconnects through reference points AE and PE and may also be referred toas an ACL/PCL bisector line.

e. Confirm Location of Tibia Reference Data

As can be understood from FIG. 72, the tibia reference data 900 zincludes reference points and reference lines that help to defineflexion/extension adjustment, varus/valgus adjustment andinternal/external rotation. For example, the tibia flexion/extensionadjustment is determined by an analysis of the sagittal images as shownin FIGS. 73A-D, which determine reference points Q1, Q1′, Q2, Q2′. Thetibia varus/valgus adjustment may be found by an analysis of FIG. 74Aand finding reference points T1, T2 and reference line T1T2. As can beunderstood from FIG. 72, the proximal reference line, line T1T2, definesthe internal/external rotation as shown in an axial view (line T1T2 inFIG. 74B) and the varus/valgus angle as shown in a coronal view (lineT1T2 in FIG. 74A).

The location of the reference points and reference lines may also beconfirmed based on their spatial relationship to each other. Forexample, as shown in FIGS. 72-73B, the anterior-posterior referencelines V1, V2 of the tibia plateau are generally parallel to the ACL/PCLbisector reference line, line AEPE. As indicated in FIGS. 72 and 74B,the axial-proximal reference line, line T1T2 is perpendicular or nearlyperpendicular to anterior-posterior reference lines V1, V2. As shown inFIG. 72, the tangent lines TQ1, TQ2, TQ1′, TQ2′ are perpendicular ornearly perpendicular to the ACL/PCL bisector reference line, line AEPE.

f. Mapping the Tibia Reference Data to an x-y Plane

As can be understood from FIGS. 75A-75B, which depict the tibiareference data 900 z on a coordinate system (FIG. 75A) and on a proximalend of the tibia (FIG. 75B), the tibia reference data 900 z is mapped toan x-y coordinate system to aid in the selection of an appropriate tibiaimplant. As shown in FIG. 75A, the endpoints Q1, Q1′, Q2, Q2′ and theirrespective anterior posterior reference lines V1 and V2 and theendpoints T1, T2 and the proximal reference line T1T2 are each mapped tothe reference plane. In addition, and as shown in FIG. 75B, thereference data 900 z may be imported onto a 3D model of the tibia 28″for verification purposes. The tibia reference data 900 z is stored forlater analysis.

2. Selecting Tibia Implant Candidate

There are six degrees of freedom for placing the tibial implant onto thetibia. The reference points and reference lines determined above willconstrain all but 2 degrees of freedom which are translated in the x-yplane. The sizing and positioning of the tibia implant (and the femoralcomponent) will be verified with a 2D view of the knee and components.

As briefly discussed above with reference to FIGS. 1A and 50B-50C, whenselecting the tibia implant model 34″ corresponding to the appropriatetibia implant size to be used in the actual arthroplasty procedure, thesystem 4 may use one of at least two approaches to select theappropriate size for a tibia implant [block 115]. In one embodiment, thetibia implant is chosen based on the size of the femoral implant thatwas determined above. In some embodiments, as discussed with referenceto FIGS. 76A-76C, the system 4 determines the tibial anterior-posteriorlength tAP and the tibial medial-lateral length tML and the tibiaimplant 34″ can be selected based on the anterior-posterior extent tAPof the proximal tibia. In some embodiments, the tibia implant may beselected based on both the tibial anterior-posterior length tAP and thetibial medial-lateral length tML.

In one embodiment, there is a limited number of sizes of a candidatetibia implant. For example, one manufacturer may supply six sizes oftibia implants and another manufacturer may supply eight or anothernumber of tibia implants. The anterior-posterior length jAP andmedial-lateral length jML dimensions of these candidate implants may bestored in a database. The tAP and tML are compared to the jAP and jML ofcandidate tibia implants stored in the database.

FIG. 76A is a 2D sagittal image slice of the tibia wherein asegmentation spline with an AP extant is shown. FIG. 76B is an axial endview of the tibia upper end of the tibia bone image or model 28″depicted in FIG. 52A. FIG. 76C is a plan view of the joint side 255 z ofthe tibia implant model 34″ depicted in FIG. 52B. The views depicted inFIGS. 76A-76C are used to select the proper size for the tibial implantmodel 34″. The tibia implant may be chosen based on the maximum tAPextent as measured in an analysis of the segmentation spine as shown inFIG. 76A.

Each patient has tibias that are unique in size and configuration fromthe tibias of other patients. Accordingly, each tibia bone model 28″will be unique in size and configuration to match the size andconfiguration of the tibia medically imaged. As can be understood fromFIG. 76B, the tibial anterior-posterior length tAP is measured from theanterior edge 335 z of the tibial bone model 28″ to the posterior edge330 z of the tibial bone model 28″, and the tibial medial-lateral lengthtML is measured from the medial edge 340 z of the medial plateau of thetibia bone model 28″ to the lateral edge 345 z of the lateral plateau ofthe tibia bone model 28″.

As can be understood from FIG. 76C, each tibial implant available viathe various implant manufacturers may be represented by a specific tibiaimplant 3D computer model 34″ having a size and dimensions specific tothe actual tibia implant. Thus, the representative implant model 34″ ofFIG. 3D may have an associated size and associated dimensions in theform of, for example, anterior-proximal extent tAP and themedial-lateral extent tML of the tibia model 34″, as shown in FIG. 76B.In FIG. 76C, the anterior-posterior extent jAP of the tibia implantmodel 34″ is measured from the anterior edge 315 z to the posterior edge310 z of the tibial implant model 34″, and the medial-lateral extent jMLis measured from the medial edge 320 z to the lateral edge 325 z of thetibial implant model 34″. Once the tibia implant candidate 34″ ischosen, the reference lines jML, jAP of the implant candidate 34″ arestored by the system 4 for later analysis.

3. Determine Tibia Implant Reference Data

As can be understood from FIG. 77, which is a top view of the tibiaplateaus 404 z′, 406 z′ of a tibia implant model 34″, wherein the tibiaimplant reference data 900 z′ is shown, the tibia reference data 900 z′may include tangent points q1, q1′, q2, q2′ and correspondinganterior-posterior reference lines V3, V4 and intersection points t1, t2and its corresponding proximal reference line t 1 t 2.

In order to define the implant reference data 900 z′ relative to thetibia model 28″, the implant reference lines jML, jAP are imported intothe same x-y plane with the tibia reference data 900 z that waspreviously mapped to the x-y plane. For gross alignment purposes, themedial-lateral extent jML of the tibia implant 34″ is aligned with theproximal reference line T1T2 of the tibia model 28″. Then, the tibiareference data 900 z′ is determined. The implant 34″ and the bone model28″ may then undergo additional alignment processes.

a. Determine Tangent Points q1, q1′, q2, q2′

As shown in FIG. 77, each tibia plateau 404 z′, 406 z′ includes a curvedrecessed condyle contacting surface 421 z′, 422 z′ that is generallyconcave extending anterior/posterior and medial/lateral. Each curvedrecessed surface 421 z′, 422 z′ is generally oval in shape and includesan anterior curved edge 423 z′, 424 z′ and a posterior curved edge 425z′, 426 z′ that respectively generally define the anterior and posteriorboundaries of the condyle contacting surfaces 421 z′, 422 z′ of thetibia plateaus 404 z′, 406 z′. Thus, the lateral tangent points q1, q1′can be identified as the most anterior and posterior points,respectively, of the curved recessed condyle contacting surface 421 z′of the lateral tibia plateau 404 z′. The medial tangent points q2, q2′can be identified as the most anterior and posterior points,respectively, of the curved recessed condyle contacting surface 422 z′of the medial tibia plateau 406 z′.

b. Determine Reference Lines V3 and V4

As can be understood from FIG. 77, line V3 extends through anterior andposterior points q1, q1′, and line V4 extends through anterior andposterior points q2, q2′. Line V3 is a lateral anterior-posteriorreference line. Line V4 is a medial posterior-anterior reference line.Each line V3, V4 may align with the lowest point of theanterior-posterior extending groove/valley that is the ellipticalrecessed tibia plateau surface 421 z′, 422 z′. The lowest point of theanterior-posterior extending groove/valley of the elliptical recessedtibia plateau surface 421 z′, 422 z′ may be determined via ellipsoidcalculus. Each line V3, V4 will be generally parallel to theanterior-posterior extending valleys of its respective ellipticalrecessed tibia plateau surface 421 z′, 422 z′. The length of thereference lines V3, V4 can be determined and stored for later analysis.

c. Determine Intersection Points t1, t2 and Implant Proximal ReferenceLine t 1 t 2

As shown in FIG. 77, the intersection or reference points t1, t2represent the midpoints of the respective surfaces of the lateral tibiaplateau 404 z′ and the medial tibia plateau 406 z′. Also, eachintersection point t1, t2 may represent the most distally recessed pointin the respective tibia plateau 404 z′, 406 z′. An implant proximalreference line t 1 t 2 is created by extending a line between thelateral and medial lowest reference points t1, t2. The length of thereference line t 1 t 2 can be determined and stored for later analysis.This line t 1 t 2 is parallel or generally parallel to the joint line ofthe knee. Also, as indicated in FIG. 77, the tibia implant 34″ includesa base member 780 z for being secured to the proximal tibia 28″.

d. Align Implant Reference Data 900 z′ with Tibia Reference Data 900 z

As can be understood from FIGS. 77 and 75A, the implant reference data900 z′ specifies the position and orientation of the tibia implant 34″and generally aligns with similar data 900 z from the tibia bone model28″. Thus, the lateral tangent points q1, q1′ and medial tangent pointsq2, q2′ of the implant 34″ generally align with the lateral tangentpoints Q1, Q1′ and medial tangent points Q2, Q2′ of the tibia 28″. Theanterior posterior reference lines V3, V4 of the implant 34″ generallyalign with the anterior posterior reference lines V1, V2 of the tibiamodel 28″. The intersection points t1, t2 of the implant 34″ generallyalign with the reference points T1, T2 of the tibia 28″. The proximalreference line t 1 t 2 of the implant 34″ generally aligns with theproximal reference line T1T2 of the tibia 28″. Reference line t 1 t 2 isapproximately perpendicular to the anterior-posterior reference linesV3, V4.

The implant reference data 900 z′ lies on a coordinate frame, plane r′.The tibia reference data 900 z lies on a coordinate frame, plane s′.Thus, the alignment of the implant 34″ with the tibia 28″ is thetransformation between the two coordinate frames plane r′, plane s′.Thus, the gross alignment includes aligning the proximal line t 1 t 2 ofthe implant 34″ to the proximal line T1T2 of the tibia 28″. Then, in afurther alignment process, the reference points t1, t2 of the implantand the reference points T1, T2 of the tibia 28″ are aligned. Theimplant 34″ is rotated such that the sagittal lines of the implant 34″(e.g. V3, V4) are parallel or generally parallel to the sagittal linesof the tibia 28″ (e.g. V1, V2). Once the tibia 28″ and the implant 34″are in alignment (via the reference data 900 z, 900 z′), the tibial cutplane can be determined.

4. Determine Surgical Cut Plane for Tibia

a. Determine Cut Plane of the Tibia Implant

The cut plane of the tibia implant is determined. The user may determinethis cut plane by a method such as one described with respect to FIGS.78A-78C. FIG. 78A is an isometric view of the 3D tibia bone model 1002 zshowing the surgical cut plane SCP design. FIGS. 78B and 78C aresagittal MRI views of the surgical tibia cut plane SCP design with theposterior cruciate ligament PCL.

During the TKA surgery, the damaged bone surface portions of theproximal tibia will be resected from the cut plane level 850 z and beremoved by the surgeon. As shown in FIGS. 78B and 78C, the surgicaltibial cut plane 850 z may be positioned above the surface where the PCLis attached, thereby providing for the maintenance of the PCL during TKAsurgery.

FIG. 79A is an isometric view of the tibia implant 34″ wherein a cutplane r1 is shown. As can be understood from FIG. 79A, the cut plane r1of the implant 34″ is the surgical tibial cut plane 850 z and is a datapoint or set of data points that may be stored in the implant database.In order to determine whether an adjustment to the cut plane r1 must bemade, the cut plane r1 of the tibia implant 34″ is aligned with thetibia 28″.

b. Determine Initial Cut Plane of the Tibia

As shown in FIG. 79B, which is a top axial view of the implant 34″superimposed on the tibia reference data 900 z, the implant 34″ isopened with the tibia reference data 900 z and is generally aligned withthe tibia reference data 900 z at the level of the cut plane r1 by thesystem 4. However, the implant 34″ is not centered relative to the tibiareference data 900 z. The anterior/posterior extent tAP″ andmedial/lateral extent tML″ of the tibia 28″ at the cut level are found.

The implant 34″ may be centered by the system (or manually by a user ofthe system). As indicated in FIG. 79C, which is an axial view of thetibial implant aligned with the tibia reference data 900 z, the tibiaimplant 34″ is then centered relative to the anterior posterior extenttAP″ and the medial lateral extents tML″ of the tibia 28″.

c. Determine Joint Line and Adjustment

In order to allow an actual physical arthroplasty implant to restore thepatient's knee to the knee's pre-degenerated or natural configurationwith the its natural alignment and natural tensioning in the ligaments,the condylar surfaces of the actual physical implant generally replicatethe condylar surfaces of the pre-degenerated joint bone. In oneembodiment of the systems and methods disclosed herein, condylarsurfaces of the bone model 28″ are surface matched to the condylarsurfaces of the implant model 34″. However, because the bone model 28″may be bone only and not reflect the presence of the cartilage thatactually extends over the pre-degenerated condylar surfaces, the surfacematching of the modeled condylar surfaces may be adjusted to account forcartilage or proper spacing between the condylar surfaces of thecooperating actual physical implants (e.g., an actual physical femoralimplant and an actual physical tibia implant) used to restore the jointsuch that the actual physical condylar surfaces of the actual physicalcooperating implants will generally contact and interact in a mannersubstantially similar to the way the cartilage covered condylar surfacesof the pre-degenerated femur and tibia contacted and interacted.

i. Determine Adjustment Value Tr

Thus, in one embodiment, the implant model is modified or positionallyadjusted (via e.g. the tibia cut plane) to achieve the proper spacingbetween the femur and tibia implants. To achieve the correct adjustmentor joint spacing compensation, an adjustment value tr may be determined.In one embodiment, the adjustment value tr that is used to adjust theimplant location may be based off of an analysis associated withcartilage thickness. In another embodiment, the adjustment value tr usedto adjust the implant location may be based off of an analysis of properjoint gap spacing, as described above with respect to FIGS. 63G and 63H.Both of the methods are discussed below in turn.

1. Determining Cartilage Thickness

FIG. 79D is a MRI image slice of the medial portion of the proximaltibia and indicates the establishment of landmarks for the tibia POPdesign. FIG. 79E is a MRI image slice of the lateral portion of theproximal tibia. The wm in FIG. 79D represents the cartilage thickness ofthe medial tibia meniscus, and the wl in FIG. 79E represents thecartilage thickness of the lateral tibia meniscus. In one embodiment,the cartilage thicknesses wl and wm are measured for the tibia meniscusfor both the lateral and medial plateaus 760 z, 765 z via the MRI slicesdepicted in FIGS. 79D and 79E. The measured thicknesses may be compared.If the cartilage loss is observed for the medial plateau 765 z, then thewlmin of lateral plateau 760 z is selected as the minimum cartilagethickness. Similarly, if the lateral plateau 760 z is damaged due tocartilage loss, then the wmmin of medial plateau 765 z is selected asthe minimum cartilage thickness. The minimum cartilage wr may beillustrated in the formula, wr=min (wm, wl). In one embodiment, forpurposes of the adjustment to the tibia, the adjustment value tr may bemay be equal to the minimum cartilage value wr.

2. Determining Joint Gap

In one embodiment, the joint gap is analyzed as discussed above withrespect to FIGS. 63G and 63H to determine the restored joint line gapGp3. In one embodiment, for purposes of the adjustment to the tibiashape matching, the adjustment value tr may be calculated as being halfof the value for Gp3, or in other words, tr=Gp3/2.

d. Determine Compensation for Joint Spacing

After centering the implant 34″ within the cut plane, joint spacingcompensation is taken into account. As shown in FIG. 79F, which is anisometric view of the tibia implant and the cut plane, the implant 34″and cut plane-r1 are moved in a direction that is generallyperpendicular to both the proximal and sagittal reference lines by anamount equal to adjustment value tr, thereby creating an adjusted cutplane r1′. In one embodiment, the adjustment value tr is equal toapproximately one-half of the joint spacing. In other embodiments, theadjustment value tr is equal to the cartilage thickness.

Thus, the implant candidate may be selected relative to the jointspacing compensation that was determined previously with reference toFIGS. 63G, 63H, 79D and 79E. As discussed above, in one embodiment, oncethe joint spacing compensation is determined, one-half of the jointspacing compensation will be factored in to the femur planning processand one-half of the joint spacing compensation will be factored in tothe tibia planning process. That is, the femur implant is adjusted by anamount equal to one-half of the joint spacing compensation. Thus, thecandidate femur implant will be chosen such that when it is positionedon the femur relative to the joint spacing compensation, the candidateimplant will approximate the pre-degenerated joint line. Similarly, thetibia implant is adjusted by an amount equal to one-half of the jointspacing compensation. Thus, the candidate tibia implant will be chosensuch that when it is positioned on the tibia relative to the jointspacing compensation, the candidate implant will approximate thepre-degenerated joint line. Also, the tibia implant mounting post 780 z(see FIG. 80B) and the femur implant mounting post 781 z (see FIG. 31A)will be oriented at approximately the center of the tibia and femur.

F. Verification of Implant Planning Models and Generation of SurgicalJigs Based on Planning Model Information

FIGS. 80A-81 illustrate one embodiment of a verification process thatmay be utilized for the preoperative planning process disclosed herein.FIGS. 80A-80C are sagittal views of a 2D image slice of the femur 28′(FIGS. 80A and 80B) and the tibia 28″ (FIG. 80B) wherein the 2D computergenerated implant models 34 are also shown. As can be understood fromFIGS. 80A-80C, verification for both the distal femur and proximal tibiais performed by checking the reference lines/planes in 2D sagittalviews. The reference lines/planes may also be checked in other views(e.g. coronal or axial). For example, and as can be understood fromFIGS. 80A and 80B, for the femur planning model, the flexion-extensionrotation is verified by checking whether the inflection point 506 z ofthe anterior cortex of the femur 28′ sufficiently contacts the interiorsurface 510 z of the anterior flange 512 z of implant 34′. That is, ascan be understood from FIG. 80A2, when the implant 34′ is properlyaligned with the femur 28′, the flange point 500 z of the implant shouldtouch the inflection point of the segmentation spline or femur 28′.

As can be understood with reference to FIG. 80B, the tibia planning maybe verified by looking at a 2D sagittal slice. Depending on the initialplanning choice made above, one of the following can be verified: 1)whether the size of the tibial implant 34″ matches or corresponds withthe size of the femoral implant 34′, or 2) whether the tibial implant34″ is one size larger or one size smaller than the femoral implant 34′size (e.g., a size 2 femur, and a size 1 tibia; or a size 2 femur, and asize 2 tibia; or a size 2 femur, and a size 3 tibia). In otherembodiments, the size of tibial implant may be chosen without takinginto account the size of the femoral implant. One of skill in the artwill recognize that different implant manufacturers may utilize adifferent naming convention to describe different sizes of implants. Theexamples provided herein are provided for illustrative purposes and arenot intended to be limiting.

As indicated in FIG. 80B, the placement of the tibial implant can beverified by viewing the anterior and posterior positions of the implant34″ relative to the tibial bone 28″. If the implant is properlypositioned, the implant should not extend beyond the posterior oranterior edge of the tibia bone. The flexion-extension of the tibia 28″can be verified by checking that the tibial implant reference line 906z, which is a line segment approximating the normal direction of theimplant's proximal surface, is at least parallel with the posteriorsurface 904 z of the tibia 28″ or converging with the posterior tibialsurface 906 z around the distal terminus of the tibial shaft.

In other embodiments, as shown in FIGS. 81A-81G and FIGS. 82A-82C, theplanning can also be confirmed from generated 3D bone models 1000 z,1002 z and 3D implant models 1004 z, 1006 z. If the planning is doneincorrectly, the reference lines 100 z, 100 z′, 900 z, 900 z′ will becorrected in the 2D MRI views to amend the planning. FIGS. 81A-81C andFIGS. 81E-81G are various views of the implant 3D models 1004 z, 1006 zsuperimposed on the 3D bone models 1000 z, 1002 z. FIG. 81D is a coronalview of the bone models 1000 z, 1002 z.

FIGS. 81A-81G show an embodiment of the POP system disclosed herein. Thealignment of the implant models 1004 z, 1006 z with the bone models 1000z, 1002 z is checked in the anterior view (FIG. 81A), the posterior view(FIG. 81E), the lateral view (FIG. 81B), the medial view (FIG. 81C), thetop view (FIG. 81F) and the bottom view (FIG. 81G).

The flexion/extension between the femur and tibia implant models 1004 z,1006 z and the femur and tibia bone models 1000 z, 1002 z is examined inboth the medial view and the lateral view. For example, FIG. 81B showsthe lateral view wherein the knee is shown in full extension or 0 degreeflexion and in its natural alignment similar to its pre-arthritis status(e.g., neutral, varus or valgus), and FIG. 81C shows the medial view ofthe knee in full extension or 0 degree flexion and in its naturalalignment (e.g., neutral, varus or valgus).

FIG. 81D shows the varus/valgus alignment of the knee model 28 m′, 28 m″with the absence of the implants 34 m′, 34 m″. The gaps Gp4, Gp5 betweenthe lowermost portions of distal femoral condyles 302 z, 303 z and thelowermost portions of the tibia plateau 404 z, 406 z will be measured inthe femoral and tibia bone models 28 m′, 28 m″. Gap Gp4 represents thedistance between the distal lateral femoral condyle 302 z and thelateral tibial plateau 404 z. Gap Gp5 represents the distance betweenthe distal medial femoral condyle 303 z and the medial tibial plateau406 z. In the varus/valgus rotation and alignment, Gp4 is substantiallyequal to Gp5, or |Gp4−Gp5|<<1 mm. FIG. 81D shows the knee model 28 m′,28 m″ that is intended to restore the patient's knee back to his pre-OAstage.

The IR/ER rotation between the femur and tibia implant models 1004 z,1006 z and the femur and tibia bone models 1000 z, 1002 z is examined inboth the top and bottom views. For example, FIG. 81F shows the top viewof the tibia showing the IR/ER rotation between no flexion and highflexion, and FIG. 81G shows the bottom view of the femur showing theIR/ER rotation between no flexion and high flexion. The stem of thetibia implant model 1006 z and the surgical cut plane of the tibiaimplant model 1006 z provide the information for the IR/ER rotation.

FIGS. 82A-82C show another embodiment of the POP system disclosedherein. FIG. 82A is an medial view of the 3D bone models. FIG. 82B is anmedial view of the 3D implant models. FIG. 82C is an medial view of the3D implant models superimposed on the 3D bone models.

As shown in FIG. 82A, a 3D model of the femur bone 1000 z and a 3D modelof the tibia bone 1002 z may be generated from the 2D segmentationsplines of image slices and the reference data 100 z, 900 z determinedabove for verification of the POP. As shown in FIG. 82B, a 3D model ofthe femur implant 1004 z and a 3D model of the tibia implant 1006 z maybe generated based on the reference lines 100 z′, 900 z′ determinedabove for verification of the POP. The implant models 1004 z, 1006 z andthe bone models 1000 z, 1002 z are aligned based on the reference linesin a 3D computer modeling environment and the alignment is checked inthe sagittal view as shown in FIG. 82C. If the alignment of the bonemodels 1000 z, 1002 z and the implant models 1004 z, 1006 z is notcorrect, the reference lines 100 z, 100 z′, 900 z, 900 z′ will becorrected in the 2D views to amend the planning.

The knee model 28′, 28″, 1000 z, 1002 z and associated implant models34′, 34″, 1004 z, 1006 z developed through the above-discussed processesinclude dimensions, features and orientations that the system 10depicted in FIG. 1A can be utilized to generate 3D models of femur andtibia cutting jigs 2. The 3D model information regarding the cuttingjigs can then be provided to a CNC machine 10 to machine the jigs 2 froma polymer or other material.

G. Mechanical Axis Alignment

While much of the preceding disclosure is provided in the context ofachieving natural alignment for the patient's knee post implantation ofthe actual physical femur and tibia implants, it should be noted thatthe systems and methods disclosed herein can be readily modified toproduce an arthroplasty jig 2 that would achieve a zero degreemechanical axis alignment for the patient's knee post implantation.

For example, in one embodiment, the surgeon utilizes a natural alignmentfemoral arthroplasty jig 2A as depicted in FIGS. 51A and 51B to completethe first distal resection in the patient's femoral condylar region.Instead of utilizing a natural alignment tibia arthroplasty jig 2B asdepicted in FIGS. 51C and 51D, the surgeon instead completes the firstproximal resection in the patient's tibia plateau region via free handor a mechanical axis guide to cause the patient's tibia implant toresult in a mechanical axis alignment or an alignment based off of themechanical axis (e.g., an alignment that is approximately one toapproximately three degrees varus or valgus relative to zero degreemechanical axis).

In one embodiment of the POP systems and methods disclosed herein,instead of superposing the 3D bone models 1000 z, 1002 z to the 3Dimplant models 1004 z, 1006 z in a manner that results in the saw cutand drill hole data 44 that leads to the production of natural alignmentarthroplasty jigs 2A, 2B, the superposing of the bone and implant models1000 z, 1002 z, 1004 z, 1006 z may be conducted such that the resultingsaw cut and drill hole data 44 leads to the production of zero degreemechanical axis alignment arthroplasty jigs or some other type ofarthroplasty jig deviating in a desired manner from zero degreemechanical axis.

Thus, depending on the type of arthroplasty jig desired, the systems andmethods disclosed herein may be applied to both the production ofnatural alignment arthroplasty jigs, zero degree mechanical axisalignment jigs, or arthroplasty jigs configured to provide a result thatis somewhere between natural alignment and zero degree mechanical axisalignment.

Although the present invention has been described with respect toparticular embodiments, it should be understood that changes to thedescribed embodiments and/or methods may be made yet still embraced byalternative embodiments of the invention. For example, certainembodiments may operate in conjunction with a MRI or a CT medicalimaging system. Yet other embodiments may omit or add operations to themethods and processes disclosed herein. Accordingly, the proper scope ofthe present invention is defined by the claims herein.

What is claimed is:
 1. A method for planning an arthroplasty procedureon a patient bone, the method comprising: accessing generic bone datastored in a memory of a computer; using the computer to generatemodified bone data by modifying the generic bone data according tomedical imaging data of the patient bone; using the computer to derive alocation of non-bone tissue data relative to the modified bone data; andsuperimposing implant data and the modified bone data in defining aresection of an arthroplasty target region of the patient bone.
 2. Themethod of claim 1, wherein the non-bone tissue data includes a contourof the non-bone tissue data.
 3. The method of claim 2, wherein thenon-bone tissue data pertains to cartilage.
 4. The method of claim 1,wherein the non-bone tissue data comprises modified non-bone tissue datathat is computer generated by accessing generic non-bone tissue datastored in the memory and using the computer to modify the genericnon-bone tissue data according to the medical imaging data of thepatient bone.
 5. The method of claim 4, wherein the modified non-bonetissue data includes a contour of the non-bone tissue data.
 6. Themethod of claim 5, wherein the modified non-bone tissue data pertains tocartilage.
 7. The method of claim 1, wherein the contour of the non-bonetissue data is used in registering the resection with the patient bone.8. A surgical method according to claim 7 and further comprisingresecting the resection into the patient bone.
 9. The method of claim 7,wherein the contour of the non-bone tissue data is used in defining aregistration surface of an arthroplasty jig, the registration surfaceregistering the arthroplasty jig with the patient bone when thearthroplasty jig is used to guide the resection in the arthroplastytarget region of the patient bone.
 10. A manufacturing method accordingto claim 9 and further comprising manufacturing the arthroplasty jig tocomprise the registration surface and a resection guide capable ofguiding the resection when the registration surface interdigitates withthe patient bone.
 11. The method of claim 1, further comprisingcomparing the modified bone data to candidate implant models stored inthe memory of the computer.
 12. The method of claim 11, furthercomprising recommending an implant model based on the comparison of themodified bone data to the candidate implant models.
 13. The method ofclaim 1, further comprising presenting the defined resection to asurgeon for review.
 14. A method for planning an arthroplasty procedureon a joint region of a patient bone, the method comprising: constructinga virtual bone model of the joint region of the patient bone, thevirtual bone model comprising a contour of soft tissue and a bonesurface; determining a location and configuration of the soft tissuerelative to the bone surface of the virtual bone model; identifying aregistration surface including at least part of the location andconfiguration of the soft tissue; superimposing a virtual implant modelover the bone surface of the virtual bone model; determining a resectionrelative to the bone surface of the virtual bone model based on thesuperimposing, the resection being adapted to facilitate an implantbeing implanted on the patient bone as part of the arthroplastyprocedure, the implant corresponding to the virtual implant model; andreferencing the resection to the registration surface.
 15. The method ofclaim 14, wherein the soft tissue includes cartilage.
 16. The method ofclaim 1, wherein the virtual bone model is computer generated byaccessing a generic bone model stored in a memory and using a computerto modify the generic bone model according to medical imaging data ofthe joint region of the patient bone.
 17. The method of claim 1, furthercomprising comparing the virtual bone model to candidate implant modelsstored in a memory of a computer.
 18. The method of claim 17, furthercomprising recommending an implant model based on the comparison of thevirtual bone model to the candidate implant models.
 19. The method ofclaim 1, further comprising presenting the resection to a surgeon forreview.
 20. The method of claim 1, wherein the virtual bone modelincludes a bone and cartilage model and a bone-only model.
 21. Asurgical method according to claim 14 and further comprising resectingthe resection into the patient bone.
 22. A manufacturing methodaccording to claim 14 and further comprising manufacturing anarthroplasty jig to comprise a mating surface and a resection guide, themating surface adapted to interdigitate with the registration surface,and the resection guide capable of guiding the resection when the matingsurface interdigitates with the patient bone.